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DOE v. GITHUB Court Filing, retrieved on January 26, 2023 is part of . You can jump to any part in this filing . This part is 11 of 21. HackerNoon’s Legal PDF Series here MEMORANDUM OF POINTS AND AUTHORITIES IV. ARGUMENT C. Plaintiffs’ Claims Fail for Reasons Specific to Each Claim. 1. Plaintiffs’ DMCA Claim Should be Dismissed. Although the complaint is replete with allegations about alleged similarities between Copilot’s output and the code it was trained on, Plaintiffs do not assert a copyright infringement claim. Instead, they allege that Defendants violated the DMCA by (1) removing or altering CMI from Licensed Materials, (2) distributing copies of Licensed Materials knowing CMI had been removed or altered without authority, and (3) knowingly providing CMI that is false by “asserting and/or implying that Copilot is the author of the Licensed Materials.” (Compl. ¶¶ 158-159.) Plaintiffs’ allegations do not meet DMCA requirements and fail properly to plead a DMCA claim. a. Plaintiffs Have Not Properly Pled a Claim for Removal of CMI. To properly plead a claim for removal of CMI, a plaintiff must plausibly allege: (1) the existence of CMI on a work, (2) removal or alteration of that information, (3) that the removal or alteration was done intentionally; and (4) the removal or alteration was done knowing or having reasonable grounds to know that it would induce, enable, facilitate, or conceal copyright infringement. 17 U.S.C. § 1202(b); Stevens v. CoreLogic, Inc., 899 F.3d 666, 673 (9th Cir. 2018) (discussing the mental state elements); O’Neal v. Sideshow, Inc., 583 F. Supp. 3d 1282, 1286-87 (C.D. Cal. 2022) (discussing other elements). Plaintiffs’ claim under § 1202(b) arises out of the allegation that CMI was removed from Plaintiffs’ code. But in order to prevent § 1202 from subsuming every copyright dispute, courts have interpreted “removal” in the § 1202 context to require that there was some identical copy of the plaintiff’s work made without the plaintiff’s CMI. (i) Failure to Allege Removal from Identical Copies. See, e.g., Kelly v. Arriba Soft Corp., 77 F. Supp. 2d 1116, 1122 (C.D. Cal. 1999) (requiring that CMI was removed from “a plaintiff’s product or original work”), aff’d and rev’d in part on other grounds, 336 F.3d 811 (9th Cir. 2003). Where a defendant makes a copy of a defendant’s work that is substantially similar, but not identical, to the plaintiff’s work, and omits CMI from that copy, there may be a claim for copyright infringement, but there cannot be a claim under § 1202. See Frost-Tsuji Architects v. Highway Inn, Inc., No. CIV. 13-00496 SOM, 2015 WL 263556, at *3 (D. Haw. Jan. 21, 2015), aff’d, 700 F. App’x 674 (9th Cir. 2017) (“But the drawing by [the defendant] is not identical to the drawing by [the plaintiff], such that this court can say that [the defendant] removed or altered [the plaintiff’s] copyright management information from [the drawing].”); id. (“basing a drawing on [the plaintiff’s] work is not sufficient to support a claim” under § 1202); Kirk Kara Corp. v. W. Stone & Metal Corp., No.CV 20-1931-DMG (EX), 2020 WL 5991503, at *6 (C.D. Cal. Aug. 14, 2020) (dismissing DMCA claim because “while the works may be substantially similar, Defendant did not make identical copies of Plaintiff’s works and then remove engraved CMI”). Here, Plaintiffs concede that Copilot does not generate identical copies: • “[T]he Output is often a near-identical reproduction of code from the training data.” (Compl. ¶ 46 (emphasis added)); • “Codex has reproduced Haverbeke’s Licensed Material almost verbatim, with the only difference being drawn from a different portion of those same Licensed Materials.” (Id. ¶ 60 (emphasis added)); • “Like the other examples above—and most of Copilot’s Output—this output is nearly a verbatim copy of copyrighted code. In this case, it is substantially similar to the “isPrime” function in the book Think JavaScript by Max X. Curinga et al., . . . .” (Id. ¶ 74 (emphasis added).) Because Plaintiffs affirmatively allege that the output at issue is not identical to the allegedly copied material, they have pled themselves out of court on the § 1202 claim, and it should be dismissed with prejudice. The § 1202 claim is also subject to dismissal because Plaintiffs have not sufficiently identified any works from which CMI was allegedly removed. See Free Speech Sys., LLC v. Menzel, 390 F. Supp. 3d 1162, 1175 (N.D. Cal. 2019) (dismissing DMCA claim because Menzel “merely alleged that his photographs ‘were altered to remove certain of [his] copyright management information’ without providing any facts to identify which photographs had CMI removed or to describe what the removed or altered CMI was”). (ii) Failure to Identify the Works. Plaintiffs merely allege generally that Defendants removed CMI from “Licensed Materials,” which they define broadly as “materials made available publicly on GitHub that are subject to various licenses containing conditions for use of those works.” (Compl. ¶¶ 1, 148.) The complaint highlights Plaintiffs’ imprecision, reciting in the DMCA cause of action that Copilot was “trained on millions— possibly billions—of lines of code.” (Id. ¶ 143.) But the few specific instances the complaint points to are not examples of Plaintiffs’ own code, but snippets from third-party programming textbooks. (Id. ¶¶ 56-61, 71-75.) That code is not the subject of Plaintiffs’ DMCA allegations, as it does not fall within the complaint’s definition of “Licensed Materials.” Without identifying specific works from which CMI was removed, Plaintiffs fail to state a claim for CMI removal. Plaintiffs also have not pled facts sufficient to meet the “double-scienter” requirement of Section 1202(b)(3), which requires “the defendant who distributed improperly attributed copyrighted material must have actual knowledge that CMI ‘has been removed or altered without authority of the copyright owner or the law,’ as well as actual or constructive knowledge that such distribution ‘will induce, enable, facilitate, or conceal an infringement.’” Mango v. BuzzFeed, Inc., 970 F.3d 167, 171 (2d Cir. 2020). (iii) Failure to Adequately Plead Scienter. “[T]he plaintiff must provide evidence from which one can infer that future infringement is likely, albeit not certain, to occur as a result of the removal or alteration of CMI.” CoreLogic, 899 F.3d at 676 (finding CoreLogic not liable for violating § 1202(b) because photographers had “not put forward any evidence that CoreLogic knew its software carried even a substantial risk of inducing, enabling, facilitating, or concealing infringement, let alone a pattern or probability of such connection to infringement”). Here, Plaintiffs have not alleged facts sufficient to establish a substantial risk that any copyright infringement has occurred or that any future infringement is likely because of the removal of CMI, nor that any of the OpenAI Entities had reason to know of any such likelihood. They have not alleged, for example, copying of protectible expression: that is, that the allegedly copied code was original, that there was no merger of idea and expression, and that the allegedly copied code did not represent “scènes à faire.” See, e.g., Oracle Am., Inc. v. Google Inc., 872 F. Supp. 2d 974, 984-997 (N.D. Cal. 2012) (summarizing the various doctrines limiting copyright in computer programs). And they would need to allege that any copying was not fair use—a heavy burden in light of the Supreme Court’s holding in the source-code context that “taking only what was needed to allow users to put their accrued talents to work in a new and transformative program . . . was a fair use of that material as a matter of law.” Google LLC v. Oracle Am., Inc., 141 S. Ct. 1183, 1209 (2021); see also, e.g., Authors Guild v. Google, Inc., 804 F.3d 202, 225 (2d Cir. 2015) (copying of millions of books for the purpose of searching them and providing relevant snippets to users was fair use). Finally, they would have to identify with specificity which work or works were copied and specify which defendant is alleged to have infringed which particular copyright. Lynwood Invs. CY Ltd. v. Konovalov, No. 20-CV-03778- MMC, 2022 WL 3370795, at *19 (N.D. Cal. Aug. 16, 2022) (dismissing claim). All of these are substantial hurdles to showing that Defendants had reason to know that they would cause or further copyright infringement. The complaint meets none of them. b. Plaintiffs Have Failed to Plead a Claim for Distributing Copies of Works from Which CMI Has Been Removed. Plaintiffs’ claim that Defendants have distributed copies of code from which CMI has been removed fails for the same reasons as its claim for removal of CMI. 17 U.S.C. §§ 1202(b)(2), 1202(b)(3). See Kirk Kara, 2020 WL 5991503, at *6 (applying same 1202(b)(1) analysis to distribution claims); Dolls Kill, Inc. v. Zoetop Bus. Co., No. 2:22-cv-01463-RGKMAA, 2022 WL 16961477, at *3-4 (C.D. Cal. Aug. 25, 2022) (concluding no DMCA violation for complaint that defendants “are distributing knockoff products” where the works were not identical and only had “certain[] similarities”); Mango v. BuzzFeed, Inc., 356 F. Supp. 3d 368, 376 (S.D.N.Y 2019), aff’d, 970 F.3d 167 (2d Cir. 2020) (in view of few decisions involving DMCA’s distribution prohibitions, looking to CMI removal caselaw for guidance). Plaintiffs have not specifically identified any such copies; the supposed copies are not identical; and Plaintiffs have not shown the requisite scienter. c. Plaintiffs Have Failed to Show that OpenAI Has Conveyed Any False CMI in Connection with Copilot Outputs. Plaintiffs’ claim that Defendants have conveyed false CMI (Compl. ¶¶ 158-159) is also fundamentally flawed. 17 U.S.C. § 1202(a). The DMCA defines CMI as any information identifying the work, its author or copyright owner, and the terms and condition of use, or “links to such information,” “conveyed in connection with copies . . . of [the] work.” 17 U.S.C. § 1202(c) (emphasis added). Courts require that the allegedly false CMI’s location suggest an association with plaintiff’s work. See SellPoolSuppliesOnline.com, LLC v. Ugly Pools Ariz., Inc., 804 F. App’x 668, 670-71 (9th Cir. 2020) (affirming grant of summary judgment against plaintiff’s false CMI claim, finding defendant’s copyright notice at the bottom of the webpage was not “conveyed in connection with” plaintiff’s photos); Logan v. Meta Platforms, Inc., No. 22- cv-1847-CRB, 2022 WL 14813836, at *8 (N.D. Cal. Oct. 25, 2022) (finding copyright notice on the bottom of each Facebook user page separated from the rest of the content insufficient to plead that Meta conveyed CMI in connection with plaintiff’s photos). Once again, Plaintiffs have not pointed to any specific code that Defendants conveyed containing false CMI, as pleading standards require. See § IV.C.1.a, supra. Moreover, Plaintiffs have not properly alleged that the OpenAI Entities have conveyed any CMI at all. Plaintiffs merely allege that “Defendants have a business practice of asserting and/or implying that Copilot is the author of the Licensed Materials.” (Compl. ¶ 158.) But that is not the same as conveying CMI “in connection with copies” of the work. And in the complaint’s examples of Codex-generated code, there is no CMI presented whatsoever. (See id. ¶¶ 49, 69.) Continue Reading . here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:22-cv-06823-JST retrieved on September 8, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. DocumentCloud.org

The DMCA Claims Against OpenAI Should Be Dismissed: Here's Why They Do Not Apply 

3. Blockchain and smart contracts This article is the third chapter of a series about web decentralization. Here we will focus on one of the most trending buzzwords of the year: . While it has real and unquestionable potential, it also has significant limits. We will have a look at the main public blockchains (meaning available to everybody) and see when it can be a clever choice in a project. the blockchain Series articles Introduction File storage Blockchain and smart contracts Databases Before going further, if you have no idea what blockchain is, watch this 2 minutes video to get the main principles: A brief history of the blockchain 1995 — The first signs For the anecdote, the first known blockchain has been created in 1995 and its ledger was… a newspaper. Stuart Haber and Scott Stornetta, two researchers in cryptography, decided to use the NOTICES & LOST AND FOUND section of the to timestamp and certify data. What is the link with the blockchain everybody is speaking about nowadays? Well, it has the same characteristics: Information is encrypted and stored in a timestamped block (here an edition of the newspaper and will be distributed to a network (newspaper readers). This process makes it almost impossible to falsify data. Indeed, one could print a backdated version of the newspaper and change its content. But it would be extremely easy to verify if a version is fake or not by looking at many archives. New York Times To be precise, the information stored in the newspaper is just a hash that allows verifying the integrity of the data that is stored "off-chain". Otherwise, they would need much more space in the newspaper! You can read more about the process . here 2009 — And God created Bitcoin Bitcoin is a created in 2009 by someone under the name Satoshi Nakamoto. Its real identity is still unknown and subject to as many debates than the street artist Banksy. to read about the best conspiracy theories. But for sure, he is probably extremely rich now as he mined about a million blocks at the very beginning (the exchange rate fluctuates a lot, but makes him multi-billionaire). decentralized digital currency Search "Satoshi Nakamoto identity" on Google What makes Bitcoin revolutionary is the fact it is decentralized: it isn't owned by any bank or organization, and anyone can be part of the network. Transactions are verified by network nodes through cryptography and recorded in a public distributed ledger called a blockchain. You might wonder how we can get a consensus without a centralized trusted entity? To achieve that, miners (a.k.a. full nodes, they are rewarded to write data) have to provide a (PoW) in order to append new data into the blockchain. A PoW is the result of a complex calculation (time and energy consuming) that can be easily verified. To sum up, a single node will spend resources to append data to the blockchain, which integrity can be verified by any node. Bitcoin uses the PoW and constantly adapt the calculation difficulty. Proof of Work HashCash The two main drawbacks of the Proof of Work consensus algorithm are the following: Mining consumes a lot of energy due to the calculation complexity. Even though the currency is purely virtual, it consumes more energy than 159 countries (not combined though). It isn't sustainable nor desirable and is even more regrettable since the computations are totally useless (they aren't helping science for instance). In reality, Bitcoin full nodes aren't that much decentralized because big mining pools have more chances to get their work rewarded over individual miners. Here is the last year pools distribution: — Last year 58% of the blocks were mined by only 4 mining pools Mining pools distribution It is vulnerable to 51% attacks. To make it simple, if a group was able to control the majority of the mining nodes, they would be able to compromise the whole system integrity. It is, of course, a very expensive attack, but given the current distribution, hackers would "simply" have to take control of a few organizations to make it possible. I won’t spend too much time on Bitcoin which is mostly limited to financial transactions (even though and ). it has a scripting language it is technically possible to write smart contracts 2015 — Ethereum extends the potential of the blockchain Vitalik Buterin, a programmer interested in Bitcoin, saw a much broader potential for the technology behind this crypto-currency. He and his team introduced a new blockchain aiming to create . Instead of sticking to a basic set of operations, like in Bitcoin's scripting language, Ethereum offers a Turing-complete language. To make it simple, that means you can theoretically solve any computational problem in a assuming you have enough computation resources. Let's be clear, you will face computational limits much faster than in most languages because executing code in Ethereum has a cost (called ) and will take more time. So it is up to the developer to keep a low complexity into the program (avoid nested loops or loops with a high number of iterations). Decentralized Applications smart contract, gas The smart contracts can be written in various languages, the most popular one being . They are then compiled in an Ethereum-specific bytecode and executed in the (EVM), similarly to Java. The concept of smart contract might still be abstract in your head. If you are curious about what does a smart contract can look like, . Solidity Ethereum Virtual Machine here are a bunch of examples When coding smart contracts, security is a primary question because you are dealing with money and a small leak could have huge impacts. One good practice is to reuse libraries that have been audited instead of writing everything by yourself. Take a look to , probably the most popular one: OpenZeppelin _OpenZeppelin is an open-source framework to build secure smart contracts. Reduce the risk of vulnerabilities in your…_openzeppelin.org OpenZeppelin Also, when dealing with Ethereum, the development pipeline can be cumbersome. offers some tools that should make your life much easier: an IDE, a testing pipeline, a simple way to upgrade your contracts (with migrations) and a tool to create a new blockchain on-the-fly for your tests. It is the Swiss army knife of the Ethereum developer. Truffle Suite _The Truffle suite of tools make dapp development easier and more consistent._truffleframework.com Truffle Suite | Sweet Tools for Smart Contracts So, Ethereum looks amazing! You can code everything on it which makes it theoretically possible to decentralize any application! Let's now have a look at the : main limitations . As for Bitcoin, Ethereum uses a and has the same environmental impact issue due to its electricity consumption. The good news is they are planning to move to a (PoS) in 2019 (project name: Casper). What's this? Well, instead of resolving time and energy consuming calculations, the miners (here called validators) will have to deposit an amount of tokens that will be held as long as they are mining. They will then be given some blocks to validate based on two factors: (the more you have to lose, the more you are considered trustworthy) and (otherwise only the richest would be able to validate blocks). Environmental disaster Proof of Work consensus algorithm Proof of Stake the amount of the stake a degree of chance . A transaction takes about . It may be acceptable for some specific use cases, but not to decentralize existing applications which are already immediate. But PoS should solve this issue by reducing the time to 1 second. Slowness 15 seconds to process . For now, Ethereum can support up to 15 transactions per second (1.3 million transactions per day). That's really low compared its potential of use. In comparison, the VISA system can handle 24,000 transactions per seconds (theoretically). The good news here is that Ethereum is aiming to increase this limit to 1 million TPS by combining 2 technological solutions: and a project named . The first one consists of splitting the network into fragments that would only process certain transactions (instead of processing them all), and the second one aims to conduct some transactions on a side chain which should free up the main chain. Casper is planned to arrive during 2019, while we will have to wait until 2020 or 2021 for sharding to show up. Scalability sharding Plasma . Every transaction has a price, called , which amount depends on the complexity of the contract. Users are used to a free and unlimited Internet, so having to pay for every transaction in your day-to-day life application may be a serious obstacle. The . In a chat application where every sent message would be a transaction, it would be quite expensive to use Ethereum! Cost gas average gas price is almost $0.01 While Ethereum has a lot to address to allow their vision, they already have a plan for almost everything. But having to wait 1 or 2 years to see these problems solved, if everything goes well, put them in a risky situation. 2018 — EOSIO, Ethereum on steroids? EOSIO (or simply EOS) has very similar goals to Ethereum. So instead of a full description, I am going to focus on what makes it different: Smart contracts are being executed within (abbreviated Wasm) virtual machine. In short, it is a new web standard allowing websites to run native code in the browser within a sandbox. Wasm could become a concurrent to Javascript for frontend development. In theory, you should be able to write EOS smart contracts in any of (but for now C, C++, and Rust seem to be the most supported). WebAssembly those languages EOS uses (DPoS) as the consensus algorithm. It's very similar to PoS, except that there is a limited number of block producers (21). Even though letting all the power to 21 actors seems very risky, it relies on the community that can vote to fire untrusted producers. It is a new paradigm because the consensus algorithm is a hybrid between democracy where people elect delegates to represent them and a deterministic algorithm between the 21 delegates (which real identities are known). Interesting fact: EOS has a (any change can be applied with 15/21 voters). Delegated Proof of Stake community-written constitution The last point allows EOS to get : up to 6000 TPS, and a transaction only takes about 1 second. much better performances EOS proposes a (no gas to pay to run a transaction), the block producers will be rewarded with 1% of the yearly inflation. fee-less economic model EOS also brings some like account/password recovery, human-readable usernames (instead of some long hexadecimal addresses) and a file storage solution. These features were usually offered by third-party services, having them integrated is an interesting experiment. higher level features One of the biggest criticisms to EOS is that it is . They are definitely doing an interesting trade-off here and it's too soon to know if this choice is viable or not. On the other hand, it will probably make . In many blockchains, some important evolutions require breaking changes which resulted in splitting the community into 2 parts: those who wanted to continue with the old system and those who accept the evolution. Having some delegates should ideally offer more agility. You can read more about the different kinds of forks . too centralized EOS evolution much simpler here , the company behind EOS has closed a $4 billion ICO to develop the ecosystem. The team behind the project is also renowned (CTO Daniel Larimer previously founded BitShares and Steemit). Their project is very ambitious as their main concurrent is already the de-facto solution for lots of blockchain projects. 2019 will definitely be an interesting year and both platforms will probably have to incentives users and developers to join them. Block.one There are many other competitors like or and I may have forgotten some major ones. Feel free to share your thoughts in the comments. HyperLedger Lisk When should you use the blockchain? First of all, even if we are focusing on because they aim to decentralize the logic of an application, you should know that there is another kind: . They are useful in a project where not anyone should be able to contribute. The limit between these two kinds is not sharp, and there are also semi-pemissioned blockchains. public (or permissionless) blockchains permissioned (or private) blockchains To sum up, here are the main benefits of a blockchain: : When using a system, you don't need to trust a third party and everything is verifiable and transparent. No central authority : Once it is written on the chain, it is theoretically impossible to change it (except in case of an attack). Data immutability : Due to the structure of the blockchain, you can know in a reliable way whether a token belongs to a user or another. Single source of truth These features make blockchain particularly interesting to build traceability systems (in a supply chain for instance). But you can also use it to build the logic of a decentralized app: It helps you to get rid of the code that usually runs on your servers. Here is a landscape of blockchain applications: Source So when should you use it? You will find lots of decision trees helping you to decide whether you should or not use a blockchain in a project. I find this one particularly relevant: More details However, I kinda disagree with the 5th question: Do you intend to store large amounts of non-transactional data as part of your solutions? If you read the , you would know that solutions exist to store large amounts of data in a decentralized way. It is then possible to store the transactions on the blockchain and some encrypted files off-chain. second chapter about file storage Blockchain also brings a lot of complexity While blockchain has a huge potential, don't forget that it will complexify your application: Evolution will be complicated Upgrading your smart contracts won't be very easy. Having a good architecture and thinking about all the features you will implement from day one is important. Security as a priority As we have seen previously, a simple mistake can cost a lot and you won't be able to fix it as easily as with a centralized server. That's why security must be a number one priority, and you should audit your smart contracts before deploying them! More complex architecture For performance and cost issues, you won't be able to handle all the logic of your application within your smart contracts. You will also often have to gather data from 3rd party API. To achieve that kind of things with the blockchain, you will have to use : Oracles An oracle, in blockchain world, is an one-way digital agent that finds & verifies real-world data & cryptographically submits this information to the querying smart contract. An oracle is not the datasource itself ; it’s a translator for information provided by a 3rd-party API that’s to be added to a blockchain. With oracles, smart contracts have a pathway to interacting with data outside of the immediate blockchain environment. — but the layer that interfaces with data-sources & the blockchain Blockchain Oracles: What They Are & Why They’re Necessary A bunch of cool blockchain projects Fizzy is a . If you subscribe to it, it will automatically gather flight data from airlines and immediately compensate you in case your plane was delayed. I like this one because it brings transparency in insurance contracts which are often extremely complicated to decode. If you are interested in the technical implementation, check out . Fizzy flight delay insurance this article FOAM is introducing a solution based on a . We already have GPS, what's the point? Well, GPS is a way for a device to know it's own location, but there is no way for another actor to verify if the coordinates provided by a device are real or not (it could easily falsify them). FOAM relies on a network of beacons that will earn tokens by validating devices location. This could be particularly interesting when used with IOT. FOAM crypto-mapping Proof of Location EXERGY is a . It allows you to buy and sell energy in a secure and transparent way from your neighbors inside a microgrid. What's interesting is that decentralization can also apply to other networks than the internet. EXERGY peer-to-peer energy network

A state of the art of decentralized web — Part 3

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Conclusion, and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science; (4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR) (5) Paul Mollière, Max-Planck-Institut für Astronomie; (6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (7) Sarah Rugheimer, Department of Physics, University of Oxford; (8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR); (9) LIFE collaboration, www.life-space-mission.com. Table of Links Abstract & Introduction Methods Results Discussion Conclusions Next Steps & References Appendix A: Scattering of terrestrial exoplanets Appendix B: Corner Plots Appendix C: Bayes’ factor analysis: other epochs Appendix D: Cloudy scenarios: additional figures 6. Next Steps Several new interesting questions and opportunities for more detailed studies arise from this work. First of all, we plan to work on a study that will take advantage of the model selection potential that Bayesian retrievals have to offer, for example by comparing retrievals including and excluding non-retrieved parameters (e.g. the CO abundance). We are also performing retrievals assuming various cloud models (Konrad et al., in prep.). Retrievals of hazy planets (see e.g. Arney et al. 2016), as well as ocean worlds, might also help us further quantify the science potential of LIFE for a variety of different planet types. Another interesting study would be to increase S/N and R to even higher values. This will not only evaluate the extreme limits of a concept like LIFE, but also help us better understand if retrievals are limited by R rather than S/N (e.g. due to unresolved narrow features at low R). It would also be useful to compare different R-S/N combinations, this time fixing the observing time. This would help us quantify the best R-S/N combination needed to optimize the characterization of a terrestrial atmosphere. Further work is needed to optimize the yield in the characterization phase of the LIFE mission concept. The estimates of the observation time needed to establish knowledge about the habitability and the presence of biologically relevant molecules in the atmosphere that we derived here are a crucial piece of information for these follow-up studies. In this work, we only used simulated data obtained with the LIFE mission. However, in the future there will likely be more information available to each system and planet. Therefore, it will be important to put this study in context with other observations. For instance, joint retrievals of reflected light data obtained with LUVOIR/HabEx at optical/near-infrared wavelengths and thermal emission spectra as obtained by LIFE would provide useful insight on the synergies between the various missions. One of the most important open questions regarding the ultimate goal of detecting extrasolar life will require to put our results in context with life detection frameworks (e.g. Green et al. 2021; Catling et al. 2018; Walker et al. 2018). Our ongoing retrieval efforts could be useful for the fine-tuning of such frameworks. These, in turn, would provide insight on the meaning and the likelihood of a potential biosignature detection, which would allow us to infer and justify the presence of life forms on another planet. This work has been carried out within the framework of the National Center of Competence in Research PlanetS supported by the Swiss National Science Foundation. S.P.Q. and E.A. acknowledge the financial support from the SNSF. P.M. acknowledges support from the European Research Council under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 832428. J.L.G. thanks ISSI Team 464 for useful discussions. Acknowledgements. E.A. carried out the analyses, created the figures, and wrote the bulk part of the manuscript. B.S.K. and D.A. wrote part of the manuscript. S.P.Q. initiated the project, guided the project and wrote part of the manuscript. Author contributions. All authors discussed the results and commented on the manuscript. This research made use of: Astropy[7] , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018); Matplotlib[8] (Hunter 2007); pandas (pandas development team 2020); seaborn [9] . Software. References Arney, G., Domagal-Goldman, S. D., Meadows, V. S., et al. 2016, Astrobiology, 16, 873 Astropy Collaboration, Price-Whelan, A. M., Sipocz, B. M., et al. 2018, AJ, 156, ˝ 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 Barstow, J. K., Changeat, Q., Garland, R., et al. 2020, Mon. Not. R. Astron. 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What Are the Next Steps in Exoplanet Research?

FTC v. Amazon Court Filing, retrieved on Sep 26, 2023, is part of . You can jump to any part in this filing . This is part 14 of 80. HackerNoon’s Legal PDF Series here c. Online superstores are not reasonably interchangeable with other online stores that lack breadth and depth of product selection 148. Online superstores are also distinct from, and not reasonably interchangeable with, online stores with limited product selection, including online stores that offer products primarily from a single brand. Whether considered individually or collectively, online stores with limited selection are not reasonable substitutes to become a shopper's preferred destination for their online purchases for a broad swath of retail goods. Shopping at numerous limited-selection online stores increases shopping costs, both for individual shopping needs and in aggregate across a customer's total purchases. Consumers' overall shopping costs would increase dramatically if they tried to replace online superstores with shopping at multiple limited-selection online stores. 149. Some consumers may prefer to shop at limited-selection online stores for certain items. For example, a consumer may turn to such an online store because it specializes in unique or niche goods not available on an online superstore, because the shopper has particular brand loyalty, because the shopper finds the online store particularly trustworthy and reliable (because, for example, it screens for counterfeit goods or fake reviews), or because the non-superstore offers specialized or expert knowledge about the items it sells. 150. Limited-selection online stores do not provide an experience that is reasonably interchangeable with an online superstore because, individually and collectively, they cannot effectively compete to become a shopper’s preferred destination for online purchases given the increased shopping costs associated with shopping at online stores that lack the breadth and depth of online superstores. 151. Online stores with a limited product selection lack breadth. A shopper who must visit multiple online stores to compile a set of desired goods across different product categories faces higher shopping costs than a shopper who can search for and complete those cross-category purchases at a single online superstore. 152. RainOrShineGolf.com—a retailer of indoor golf simulator equipment—is an illustrative example of an online store that lacks the breadth of an online superstore. Golf simulator equipment such as golf ball launch monitors, mats, nets for hitting balls, and software to analyze performance collectively allow a customer to practice golf indoors. While Rain or Shine Golf and Amazon both sell indoor golf simulator equipment, they offer consumers different shopping experiences and a vastly different overall product due to the difference between the breadth of product selection at each online store. Shoppers may choose Rain or Shine Golf for occasional category-specific purchases, but due to its limited breadth it could not become a consumer’s preferred destination for a broad swath of other online purchases. 153. Unlike limited-selection online stores, an online superstore offers a single destination for a shopper to browse, buy, and return to for repeat purchases of a much wider array of goods. On an online superstore like Amazon, shopping for a golf simulator may also yield cross-category suggestions for accessories like golf gloves, golf clubs, or golf bag push carts. Moreover, if the need arises or mood strikes, a consumer shopping on an online superstore like Amazon could resupply the correct size of kitchen trash bags they previously purchased and add a new board game that the online superstore recommends based on their prior shopping behavior, all during a single shopping session. By contrast, a consumer who uses Rain or Shine Golf to buy a golf simulator but would also like to make a set of additional purchases would need to visit and do business with numerous other online stores. Those visits would incur the added shopping costs of finding those additional items, completing the various purchase processes with different logins and credentials (if the shopper can remember them), and arranging for multiple deliveries. 154. Many online stores that lack breadth of product selection also lack depth, especially online stores that primarily or exclusively feature their own brands. A shopper forced to visit multiple online stores to find the specific item that matches their needs faces higher shopping costs than a shopper who can compare across a depth of options for that item on an online superstore. 155. Tumi.com is another illustrative example. Shoppers can purchase a range of luggage, backpacks, and bags at Tumi.com, but the items sold at Tumi.com are primarily Tumi’s own brand, limiting the depth of options for any particular item. By contrast, a shopper looking for luggage on an online superstore like Amazon can browse across options from a wide variety of brands that may include Tumi as well as other brands. The shopper can peruse these options by filtering across features like brand, price point, size, and colors without incurring the additional search costs present in visiting all of the online stores operated by each brand. 156. Furthermore, the breadth and depth of product selection on online superstores increases access to valuable cross-category consumer data. This data amplifies the ability of online superstores to provide shoppers with tailored and personalized shopping experiences. As an online superstore, for example, Amazon recognizes in internal documents that (redacted) 157. These additional capabilities of online superstores influence consumers' shopping behavior. (Redacted) 158. Because limited-selection online stores do not have the same breadth and depth of selection offered by online superstores, they have access to less consumer data across categories and cannot replicate the personalization features of online superstores, reducing the ability of limited-selection online stores to compete with online superstores. 159. Online superstores treat rival online superstores differently than limited-selection stores. For example, Amazon does not allow other online superstores like Walmart.com to sell through Amazon. Yet Amazon encourages hundreds of thousands of sellers—including well-known brands that sell through their own online stores or limited-selection online stores—to do so. (Redacted) Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 2:23-cv-01495 retrieved on October 2, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ftc.gov

Product Variety Puts the Super in Online Superstores 

In this article, we will explore how to create, build and deploy every component behind this Bikes recommendation website: 🔗 demo 🎯 Goal High-level The idea behind this project is to test the opportunity to build a recommendation system using , (ML) models, and only resources. public data unsupervised machine learning free To achieve this we will: Leverage to gather the data Reddit as ML framework Spacy transformers to run the ML model, to host the back-end, to host the front-end Google Colab Heroku GitHub pages Implementation is a mountain bike ( ) recommendation system. In other words, you could choose a bike brand or model that you like and then the system will suggest 3 bikes considered interesting and related to your chosen bike. Garrascobike MTB when people talk about some on the same subreddit thread those bikes . So we could extract the names and/or brands from one thread’s comments and intersect that information with other Reddit threads with similar bikes. The idea behind the recommendation system is: Bikes should be related in some way Bike’s 📢 Foreword The goal of this guide is to chase all the aspects involved in the creation of a Web App that serves a simple recommendation system, . trying to keep the complexity level lower as possible So the technical level of this experiment won’t be too deep and we don’t follow industrial-level best practices, nevertheless, this is the guide I would like to have one year ago before starting a simple project: create a WebApp with an ML model at is core. 🗺️ Roadmap What we will do could be summarized in the following steps: Download text comments from Reddit 🐍 Extract interesting entities from the comments 🐍🤖 Create a simple recommendation system model 🐍🤖 Deploy the model on a back-end 🐍 Create a front-end that expose the model predictions 🌐 🐍 = blocks that use Python 🤖 = blocks with Machine Learning topics involved 🌐 = blocks that use HTML, CSS & Javascript 🚵‍♀️ Garrascobike The project is structured in five major sections: scraping, entities extraction, ML train, back-end, front-end (and they coincide with the five chapters of this post). On the above image, the first four sections (front-end is excluded) are reported with their dependencies and where necessary the platform where the code should be executed. Let’s dive deep into each component. 1. Download text comments from Reddit 🐍 Intro First of all: we need the data and Reddit is an amazing social network where people talk about topic. Moreover, expose some API that python packages like could use to scrape the data. any Reddit praw Prerequisites We need to choose some that talk about the topic on which we would create the recommendation system subreddits In we want to build a Mountain Bike recommendation system, so we need subreddits that talk about those bikes. Garrascobike The subreddits selected for this PoC project are: Whichbike, MTB, Mountainbiking, Bicycling How to 👨‍💻Code to use hosted on Github: subreddit-comments-dl Follow this guide to download Reddit comments: https://towardsdatascience.com/how-download-subreddit-comments-f79557c99170 More information is also on my blog: https://www.pistocop.dev/posts/subreddit_downloader/ Outcome Two CSV files with all the information about the subreddit comments. We will create two CSV files, if you are not familiar with Reddit could be useful to read this little glossary comments.csv Contain all the comments with related information, e.g. the comment’s text, the creation date, the and thread where the comment was written subreddit submissions.csv Contain all the information about the : a submission is a post that appears in each subreddit submissions 2. Extract interesting entities from the comments 🐍🤖 Intro Entities are words or sections of a text that belong to some pre-defined “family” e.g. from a text like “ ” we would like to extract something like "2019” is a and "Enduro” is a I have a 2019 Enduro DATE PRODUCT The entities extraction is related to an ML task called Named Entity Recognition (NER), for more examples see the official Spacy webpage So this block get the Reddit comments and will extract the Bikes names contained within them ( ) extraction.parquet That information will be uploaded to an instance Elasticsearch Prerequisites The comments extracted from the desired subreddit How many comments? More comments are directly proportional to better performances For this project, we have downloaded comments from 800.000 103.159 submissions How to 👨‍💻Code to use hosted on Github: garrascobike-core Extract the entities Steps Clone the repository https://github.com/pistocop/garrascobike-core Copy the scraped data under /data/01_subreddit_extractions/ Run the extraction with the script /garrascobike/01_entities_extraction.py Under the hood the script will use system with a ML model Spacy NER spacy-transformers 💡Tip: use this to run the above tasks Colab Notebook Because the process will use a machine-learning model it’s suggested to execute the code on a machine with a GPU. If you aren’t rich like me there is a big chance that you haven’t a powerful GPU for ML tasks, so use the above notebook and leverage the Google GPU’s for free 💘 Load on Elasticsearch What and why is a documental NoSQL database that could store data even if we don’t know the data structure Elasticsearch Why we should use Elasticsearch (ES)? It wasn’t mandatory to use this technology if we remain in the zone, and a python script could be enough to do the same job required for this project. POC However, use ES pledge us with the opportunity to be ready if we go out of the POC and/or we want to reuse the data in different ways for other projects. ES was used during the first data scouting phase, when wasn’t clear which data the ML model has extracted and if it’s possible to shape that information to our requirements Using ES on this scouting phase was very helpful to data discovery and express queries like “ ”, “ ” or “ ” how many threads exist with at least 1 product entity get all the entities typer list all the PRODUCT entities Run the Elasticsearch service locally Install Docker Following to run a mini-cluster locally, the cluster is suggested this guide 01_single-node Load the data Run the script script providing the parquet file and the ES endpoints /garrascobike/02_es_uploader.py # folder garrascobike-core
$ python garrascobike/02_es_uploader.py --es_index my_index \
                                        --es_host http://localhost \
										--es_port 9200 \
                                        --input_file ./data/02_entities_extractions/extraction.parquet 3. Create a simple recommendation system model 🐍🤖 Intro The parsed comments are now associated with text snippets named from the entities extraction process product After a look at those snippets, we could see that the entities here are the Bikes names and brands that users on subreddits talk about product 🦂 Of course there is also noise labelled as e.g. “canyon/giant” is a snippet that contains two brands and should not be included and/or split into two entities “canyon” and “giant”. Moreover, “12speed” isn’t a brand but the number of gears of the bike. product, For this toy project, we don’t care about the noise, so the predictions may result in suggesting bikes that don’t exist. In a real-world scenario, we should apply some revision to those products, e.g. search each brand on an images search engine and see if a bike picture is returned. Nevertheless, some brands cleaning and filtering is actually performed - code link The recommendation system will be built around the intersection of bikes brands and the comments threads to which the comments belong We will create a semifinished product: (new code version store a file named this file contain the entities found for each subreddit correlations.npz presences.csv), product thread In the last step, will train ad store an AI model based on the KNN algorithm able to make the bikes recommendations process 04_recommendation_trainer.py More about this process could be found in those good articles: https://towardsdatascience.com/how-did-we-build-book-recommender-systems-in-an-hour-part-2-k-nearest-neighbors-and-matrix-c04b3c2ef55c https://www.analyticsvidhya.com/blog/2020/11/create-your-own-movie-movie-recommendation-system/ Finally, we will upload by hand (no automatic script provided) the files create by 04_recommendation_trainer.py Use is suggested and supported by the next chapter section, it provides 10 GB of storage for free backblaze.com Prerequisites An ES instance running with the entities extracted products An account on backblaze.com How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-core Run the script, parameters: 03_correlation_extraction.py the Elasticsearch instance address es_host: the Elasticsearch instance port es_port: the Elasticsearch indexes names. They could be more than one because it’s possible to join more entities extraction. es_index_list: However, for this guide, we could use only one parameter: my_index. $ python garrascobike/03_correlation_extraction.py --es_host localhost \
                                                   --es_port 9200 \
                                                   --es_index_list my_index Run the script, parameters: 04_recommendation_trainer.py : the path to the file created by presence_data_path 03_correlation_extraction.py the model to use to build the recommendation system, only is currently available ml_model: knn : where to save the recommendation system model files, those files should be then uploaded on output_path backblaze $ python garrascobike/04_recommendation_trainer.py --presence_data_path ./data/03_correlation_data/presence_dataset/20210331124802/presences.csv \
                                                   --output_path ./data/04_recommendation_models/knn/ Outcome The recommendation system model trained to predict bikes suggestion from one bike name provided All the recommendation model files should be uploaded on Backblaze 4. Deploy the model on a back-end 🐍 Intro To make the recommendation we need a machine that runs the prediction model We will wrap the model inside a back-end that: Is in Python and use framework FastAPI Run on free Dyno as the hardware provider heroku Run for 30m and then go to sleep mode If it’s in the sleep node, need ~1m to wake up and be ready to use The back-end will expose those API: that take a bike name and return 3 bikes suggested /recommender/{bike_name} that return the list of supported bike names /brands/available that returns a timestamp and will be used to check if the back-end is up and in running state /health Prerequisites We need the list of bikes that the recommendation system model can manage: brands.csv The recommendation model that the back-end will use to make the predictions How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-be Set the correct credentials and path according to your backblaze account: To set the Backblaze bucket name and path modify the file at: /garrascobike_be/ml_model/hosted-model-info.json To set the Backblaze connection credentials modify and rename to the file: .env /garrascobike_be/.env_example Create an account on and publish the back-end: follow this guide for more detailed information: heroku 🔗 https://towardsdatascience.com/how-to-deploy-your-fastapi-app-on-heroku-for-free-8d4271a4ab9 Outcome A back-end up and running on Heroku, you could get the API URL from the “open app” button inside the Heroku webpage The back-end openAPI specification could be found under and will look like this: https://<heroku-app-url>/docs Note: you may need to wait ~1m before seeing this OpenAPI specification 5. Create a front-end that expose the model predictions 🌐 Intro We need a website that the user could use to choose the desired bike and receive the suggestions The website must use the API exposed by the back-end However, for this reason, the FE need to show some kind of “wait disclaimer” while Heroku wake up the App The FE should also provide succinctly the list of bikes that the user could choose from We will build an autocomplete text field using the amazing framework autoComplete.js We will create a static website with HTML, CSS & JS The website is then hosted for free on GitHub Pages Prerequisites The Garrascobike back-end up and running online The URL of the back-end program to run the website locally - live-server npm How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-fe Steps to set up a new front-end Fork the project Replace with your back-end URL this line Commit the changes Follow to enable the GitHub pages service on your repository (note: the repo must be public!) this guide Follow the link generated by the GitHub interface Enjoy your new front-end! Outcome Demo of the website: https://pistocop.github.io/garrascobike-fe/ The website you will see should resemble like this: 🎌 Other languages All the project is built and work for English Subreddits Other languages could be supported, the only section that requires a change is the spacy model in the entities extraction task https://github.com/pistocop/garrascobike-core Spacy transformers models of different languages could be found here: language support 💭 Final Thoughts We have just seen how to build a recommendation system from the scraping process to a WebApp. All the data are scraped from Reddit and processed with unsupervised ML models (Spacy), using the Google Colab platform. This has saved us a lot of work and supplied a straightforward path that could be automatized also. Finally, a back-end free hosted on Heruko and a front-end free hosted on GitHub pages complete the project thanks to a web app page. From here, a lot of work could be done to refine each of the components, e.g. if we want to move from a POC project like this to production and more serious system we should: Filter and remove all the entities extracted that aren’t bikes Scrape and process more data Build and test different parameters or families for the recommendation system As final, I want to say that it was nice to build this project, I haven’t experience with the front-end world and the simple website is made after following free course. theodinproject.com 🍺 And now, after completing every component and the is up and running, I must admit that I feel satisfied, hope you’ve enjoyed the journey! website **📧 Found an error or have a question? let’s [connect](https://www.pistocop.dev/)** This article was first published here: https://www.pistocop.dev/posts/garrascobike/

How a recommendation system Web App was Built

Google's Monopoly: Reduced Competition Reduces Search Quality And The Options Available To Consumers

Authors:
(1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu);
(2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work;
(3) Yixu Chen, Amazon Prime Video;
(4) Yongjun Wu, Amazon Prime Video;
(5) Hai Wei,  Amazon Prime Video;
(6)Sriram Sethuraman, Amazon Prime Video;
(7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction
Related Work
Details of Subjective Study
Subjective Analysis
Objective Assessment
Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Authors: Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction Related Work Details of Subjective Study Subjective Analysis Objective Assessment Conclusion, Acknowledgment and References Abstract and Introduction Abstract and Introduction Related Work Related Work Details of Subjective Study Details of Subjective Study Subjective Analysis Subjective Analysis Objective Assessment Objective Assessment Conclusion, Acknowledgment and References Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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