Moot

moot

OpenAI became an overnight household name, at least in tech circles, by releasing ChatGPT in November 2022. Businesses and individuals quickly adopted its tools and began using them to automate daily tasks. One issue with AI-generated text and images is that they can mirror existing works. Some have accused the software of plagiarism, while others feel it is not as detailed as human-created work. Employers everywhere want better ways to detect AI usage to meet target audiences’ needs and know the best time to utilize the technology. Open AI responded by creating an AI detection tool but has not yet released it. Many want to know the reason for the holdup and if it will ever be made available to the public. Why Is OpenAI Hesitant to Release Its Tool? TechCrunch published an article explaining OpenAI's detection tool, which is meant to catch students who cheat by having the computer program write their papers. The company has sat on it rather than releasing it, making some question why. A representative for OpenAI said the company is considering the broader impacts of the tool and taking its time. Many other detection tools have been ineffective, creating false positives at times and failing to detect AI-generated text at others. OpenAI's product would focus mainly on detecting text watermarked by ChatGPT. One reason for not releasing it is fear that it could negatively impact nonnative English speakers. The tool also forces ChatGPT to change how it works when adding the watermarked words. Broader Uses of the Tool Although the detection software is meant to catch students using AI to write papers, it could be applied to many other situations, particularly in combating fraud at larger-scale levels. For example, consider how useful an AI detector tool could be in catching computer-generated text used by cybercriminals. In 2023 alone, ransomware payments doubled from the year before to more than $1 billion dollars, and this number is expected to continue rising dramatically with the prevalence of AI. As consumers and companies work to combat data breaches, AI could make it easier to spot computer-generated text. Many scams include flowing verbiage meant to tug at someone’s emotional heartstrings or cause fear. When unsure whether a message is legitimate, people could run the text through the detection tool to see if AI was used to generate it. This could be quite valuable since many scams involve robot-written text. Arguments Against Using Detection Tools So far, AI detection tools haven’t been very accurate. They’ve falsely flagged original content as AI-generated and caused students to have to fight for their grades and sometimes their academic careers. Universities and employers take plagiarism seriously, so a false accusation could destroy someone’s future or livelihood.The tools may also be ineffective when users can__plug into easy-to-use and versatile software__ that helps them bypass AI detection, such as HumanizeAI and RealWriter. People should learn to utilize AI in ways that allow them to brainstorm and generate fresh ideas without plagiarizing information. However, they may shy away from using any type of software for fear of being flagged by a detector. Opponents of the software fear it may drive people to be less creative for fear of triggering a detector. Programs like StealthAI will rewrite AI-generated text to sound more human. They also say people will turn away from commonly used tools like ChatGPT and seek ones without the same watermarks, effectively rendering the detector tool useless. People also express concerns over bad players using the tool to reverse engineer code and create something that will utilize AI to create deep fake videos or DDoS attacks. Cybercriminals are already tricky. No one wants to give them more ways to fool people into sharing their personal data. Benefits of an AI Detection Tool Arguments against AI detection tools may soon be a moot point. While people expressed concern over nonnative English speakers getting left behind, software developers will likely fix language barrier problems as time passes. Many believe OpenAI will soon release its tool. Reports indicate that internal debate has been going on for nearly two years about the usefulness of the software versus concerns about inclusivity. Some see the benefits of universities and employers using AI-detection tools. As AI detector development grows, expect to see it used for things such as: Identifying fake videos used in political campaigns


Finding misinformation and marking it more accurately than other bots do


Generating ethical content Releasing the tool could inspire other brands to create their own programs and spur the success rate of AI detection. The Future of AI Use Love it or hate it, AI is here to stay — although companies have been a bit slower to embrace it than first predicted. MIT released a report showing that the technology is being adopted at different rates based on sectors. For example, most businesses adopting AI were in manufacturing and health care. As marketing departments learn more about how AI works and ways to implement it, expect more brands to use it and seek detector tools to prevent privacy issues and copying. Those who use AI for a while start to notice patterns that can be detected without the help of software. For example, the intro to nearly every article has similar language. The layout, pacing, and details are the same. With a bit of practice, most people can spot an AI-generated piece fairly accurately. Why People Need the Tool Now Without a way to detect AI-generated content accurately, people may distrust what they read online. In other situations, they might suspect someone used AI without disclosing it but find it impossible to prove. Workers may be falsely accused or lose their jobs and degrees when they aren’t guilty. The tool will solve the issue of students doing very little work and being lackluster in their studies but still obtaining an advanced degree based on someone else’s hard work, sweat, and tears. OpenAI’s product will likely have the most impact in the education sector. However, companies can also utilize it to ensure they produce unique, highly targeted content customers want to read. A Complex Problem OpenAI’s decision to sit on its detection tool is rooted in wanting to do what is best as AI technology grows and ethical concerns arise. It will likely release the software at some point, to the relief of educators everywhere. How other industries might use it remains to be seen. All companies developing AI programs should balance the pros and cons of each feature they introduce. Only then will AI become an indispensable tool that spurs progress. OpenAI became an overnight household name, at least in tech circles, by releasing ChatGPT in November 2022. Businesses and individuals quickly adopted its tools and began using them to automate daily tasks. One issue with AI-generated text and images is that they can mirror existing works. Some have accused the software of plagiarism, while others feel it is not as detailed as human-created work. Employers everywhere want better ways to detect AI usage to meet target audiences’ needs and know the best time to utilize the technology. Open AI responded by creating an AI detection tool but has not yet released it. Many want to know the reason for the holdup and if it will ever be made available to the public. Why Is OpenAI Hesitant to Release Its Tool? TechCrunch published an article explaining OpenAI's detection tool, which is meant to catch students who cheat by having the computer program write their papers. The company has sat on it rather than releasing it, making some question why. A representative for OpenAI said the company is considering the broader impacts of the tool and taking its time. broader impacts of the tool broader impacts of the tool Many other detection tools have been ineffective, creating false positives at times and failing to detect AI-generated text at others. OpenAI's product would focus mainly on detecting text watermarked by ChatGPT. One reason for not releasing it is fear that it could negatively impact nonnative English speakers. The tool also forces ChatGPT to change how it works when adding the watermarked words. Broader Uses of the Tool Although the detection software is meant to catch students using AI to write papers, it could be applied to many other situations, particularly in combating fraud at larger-scale levels. For example, consider how useful an AI detector tool could be in catching computer-generated text used by cybercriminals. In 2023 alone, ransomware payments doubled from the year before to more than $1 billion dollars, and this number is expected to continue rising dramatically with the prevalence of AI. As consumers and companies work to combat data breaches, AI could make it easier to spot computer-generated text. doubled from the year before doubled from the year before is expected to continue rising is expected to continue rising Many scams include flowing verbiage meant to tug at someone’s emotional heartstrings or cause fear. When unsure whether a message is legitimate, people could run the text through the detection tool to see if AI was used to generate it. This could be quite valuable since many scams involve robot-written text. Arguments Against Using Detection Tools So far, AI detection tools haven’t been very accurate. They’ve falsely flagged original content as AI-generated and caused students to have to fight for their grades and sometimes their academic careers. Universities and employers take plagiarism seriously, so a false accusation could destroy someone’s future or livelihood. The tools may also be ineffective when users can__ plug into easy-to-use and versatile software __ that helps them bypass AI detection, such as HumanizeAI and RealWriter. People should learn to utilize AI in ways that allow them to brainstorm and generate fresh ideas without plagiarizing information. However, they may shy away from using any type of software for fear of being flagged by a detector. plug into easy-to-use and versatile software Opponents of the software fear it may drive people to be less creative for fear of triggering a detector. Programs like StealthAI will rewrite AI-generated text to sound more human. They also say people will turn away from commonly used tools like ChatGPT and seek ones without the same watermarks, effectively rendering the detector tool useless. rewrite AI-generated text rewrite AI-generated text People also express concerns over bad players using the tool to reverse engineer code and create something that will utilize AI to create deep fake videos or DDoS attacks. Cybercriminals are already tricky. No one wants to give them more ways to fool people into sharing their personal data. utilize AI to create deep fake videos utilize AI to create deep fake videos Benefits of an AI Detection Tool Arguments against AI detection tools may soon be a moot point. While people expressed concern over nonnative English speakers getting left behind, software developers will likely fix language barrier problems as time passes. Many believe OpenAI will soon release its tool. Reports indicate that internal debate has been going on for nearly two years about the usefulness of the software versus concerns about inclusivity. Some see the benefits of universities and employers using AI-detection tools. that internal debate has been going on that internal debate has been going on As AI detector development grows, expect to see it used for things such as: Identifying fake videos used in political campaigns Finding misinformation and marking it more accurately than other bots do Generating ethical content Identifying fake videos used in political campaigns Identifying fake videos used in political campaigns Finding misinformation and marking it more accurately than other bots do Finding misinformation and marking it more accurately than other bots do Generating ethical content Generating ethical content Releasing the tool could inspire other brands to create their own programs and spur the success rate of AI detection. The Future of AI Use Love it or hate it, AI is here to stay — although companies have been a bit slower to embrace it than first predicted. MIT released a report showing that the technology is being adopted at different rates based on sectors. For example, most businesses adopting AI were in manufacturing and health care. adopted at different rates adopted at different rates As marketing departments learn more about how AI works and ways to implement it, expect more brands to use it and seek detector tools to prevent privacy issues and copying. Those who use AI for a while start to notice patterns that can be detected without the help of software. For example, the intro to nearly every article has similar language. The layout, pacing, and details are the same. With a bit of practice, most people can spot an AI-generated piece fairly accurately. Why People Need the Tool Now Without a way to detect AI-generated content accurately, people may distrust what they read online. In other situations, they might suspect someone used AI without disclosing it but find it impossible to prove. Workers may be falsely accused or lose their jobs and degrees when they aren’t guilty. The tool will solve the issue of students doing very little work and being lackluster in their studies but still obtaining an advanced degree based on someone else’s hard work, sweat, and tears. OpenAI’s product will likely have the most impact in the education sector. However, companies can also utilize it to ensure they produce unique, highly targeted content customers want to read. A Complex Problem OpenAI’s decision to sit on its detection tool is rooted in wanting to do what is best as AI technology grows and ethical concerns arise. It will likely release the software at some point, to the relief of educators everywhere. How other industries might use it remains to be seen. All companies developing AI programs should balance the pros and cons of each feature they introduce. Only then will AI become an indispensable tool that spurs progress.

OpenAI Made an AI Detection Tool, So Why Isn’t It Releasing It?

ওপেনএআই একটি এআই সনাক্তকরণ সরঞ্জাম তৈরি করেছে, তাহলে কেন এটি প্রকাশ করছে না?

OpenAI hat ein KI-Erkennungstool entwickelt. Warum wird es nicht veröffentlicht?

OpenAI creó una herramienta de detección de IA, ¿por qué no la publica?

OpenAI a créé un outil de détection d’IA, alors pourquoi ne le publie-t-il pas ?

ओपनएआई ने एआई डिटेक्शन टूल बनाया है, तो फिर वह इसे जारी क्यों नहीं कर रहा है?

OpenAI は AI 検出ツールを作成したのに、なぜ公開しないのか?

OpenAI가 AI 감지 도구를 만들었는데, 왜 출시하지 않을까?

A OpenAI criou uma ferramenta de detecção de IA, então por que não a lança?

Компания OpenAI создала инструмент обнаружения ИИ. Почему же она его не выпускает?

OpenAI Bir Yapay Zeka Tespit Aracı Geliştirdi, Peki Neden Yayınlamıyor?

OpenAI đã tạo ra công cụ phát hiện AI, tại sao lại không phát hành?

OpenAI 制作了一个 AI 检测工具，那么为什么不发布它呢？

SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 3 of 5. II. Disgorgement A. Legal Standard In addition to injunctive relief, a district court may also order disgorgement, which “serves to remedy securities law violations by depriving violators of the fruits of their illegal conduct.” SEC v. Contorinis, 743 F.3d 296, 301 (2d Cir. 2014). Disgorgement is an equitable remedy, permissible only where it “does not exceed a wrongdoer’s net profits and is awarded for victims.” Liu v. SEC, 591 U.S. 71, 75 (2020). [6] Following Liu, the Second Circuit clarified the meaning of “victims” in SEC v. Govil, 86 F.4th 89 (2d Cir. 2023). There, the defendant caused his company “to engage in three fraudulent securities offerings,” representing to investors that the company “would use the proceeds from the transactions to satisfy outstanding debts and for general corporate purposes.” Id. at 93. “Instead, he diverted over $7.3 million of the offering proceeds to his own private accounts.” Id. The district court determined that disgorgement was warranted, reasoning that the defrauded investors were victims of the defendant’s conduct because they were lied to, even if they “may not have been financially harmed” as a result of the lie. Id. at 97. The Second Circuit reversed, holding that “a ‘victim’ for purposes of § 78u(d)(5) is one who suffers pecuniary harm from the securities fraud.” Id. at 102. The Circuit explained that an equitable remedy is meant to “restore[] the status quo”—in the context of disgorgement, by “returning the funds to victims.” Id. at 103 (quoting Liu, 591 U.S. at 80, 88) (cleaned up). Allowing “defrauded investors who suffered no pecuniary harm . . . to receive the proceeds of disgorgement,” the Circuit reasoned, would “confer[] a windfall on those who received the benefit of the bargain.” Id. at 103. Therefore, for purposes of ordering disgorgement, it is not enough that investors are lied to and “thus denied the right to make an informed decision when considering whether to make [an] investment.” Id. at 105. “[O]ffending that right” does not result in pecuniary harm.” Id. (citing Ciminelli v. United States, 598 U.S. 306, 315 (2023)) B. Application The parties dispute whether the Circuit’s decision in Govil bars disgorgement of Ripple’s profits from Institutional Sales. The Court finds that it does. The SEC contends that ordering disgorgement would not run afoul of Govil because Ripple’s violation of Section 5, via “entirely tainted transactions, caused pecuniary harm to investors.” SEC Mem. at 17. Specifically, the SEC argues that Ripple sold XRP to certain Institutional Buyers at “deep undisclosed discounts” as high as [redacted]. Id. at 18. Because Ripple failed to publicly register these discounted Institutional Sales, the SEC argues, “non-favored investors” were deprived of “the information they needed to determine whether Ripple was treating them worse and whether they could purchase [] XRP at a better price.” Id. The SEC estimates that if Ripple had disclosed its “significant discounts to other investors,” non-favored investors could have saved “potentially more than [redacted].” Id. at 19–20. The SEC further contends that the discounted sales caused pecuniary harm by “put[ting] downward pressure on the market price of XRP,” as investors who bought discounted XRP could sell for below-market prices and still profit. Id. at 20. Govil forecloses this argument. The SEC contends that Ripple “appears to have not uniformly disclosed the disparate prices and discounts offered” to Institutional Buyers. Id. at 18. The Circuit held, however, that “the right to make an informed decision when considering whether to make [an] investment” is not a property interest that can be vindicated through disgorgement. Govil, 86 F.4th at 105. As Ripple argues, many of the Govil investors would have likely been dissuaded from investing—or, at least, valued the shares at a lower price—had they known that the company’s founder intended to pocket the funds for his own use. See Ripple Opp. at 20. Yet the Circuit found that the pecuniary-harm requirement was not satisfied, as the district court acknowledged that “the investors received the return on the investment contemplated at the outset.” Govil, 86 F.4th at 105. Here, the SEC offers only speculative evidence that the Institutional Buyers did not “receive[] the return on the investment contemplated.” Id; see also id. at 104 n.16 (noting that “whether the investors actually suffered pecuniary harm would depend on the type of securities held, the terms of those securities, and when those securities were sold,” a level of detail missing from the record in Govil). The SEC’s [redacted] estimate of lost potential savings comes from an analysis conducted by SEC accountant Andrea Fox, who calculated “how much less Institutional Buyers in the aggregate would have paid for XRP if the best price offered to Institutional Buyers had been obtained by every Institutional Buyer.”[7] Fox Decl. ¶ 34, ECF No. 946. But, the SEC does not (and cannot) establish that Ripple would have, in fact, offered any additional discounts to investors had it complied with Section 5’s registration requirements. It is possible that some investors “could have demanded and potentially paid lower prices.” SEC Mem. at 18. Yet, it is at least equally possible that Ripple would have opted to offer fewer and smaller discounts overall. Cf. id. at 17 (explaining that “unregistered shares . . . are [generally] worth less” than their registered counterparts). The fact that some investors “may have been able to negotiate better deals if discounts [offered to others] were disclosed as required,” SEC Mem. at 19, does not establish that they suffered pecuniary harm by paying the sticker price. Cf. Dura Pharms., Inc. v. Broudo, 544 U.S. 336, 342 (2005) (noting that in fraud-on-the-market cases, “an inflated purchase price will not itself constitute or proximately cause the relevant economic loss”).[8] Relying on SEC v. Ahmed, 72 F.4th 379 (2d Cir. 2023), the SEC also argues that disgorgement is appropriate because Ripple’s failure to disclose the discounts “tainted” each of the Institutional Sales. SEC Reply at 9, ECF No. 961. In Ahmed, the investment-manager defendant “stole over $65 million” from his employer and ten portfolio companies, including by negotiating transactions in which he had an undisclosed conflict of interest. 72 F.4th at 390. The Second Circuit found that the conflicted transactions were “entirely tainted,” holding that the defendant was obligated to disgorge the full amount he received. Id. at 397. But the Circuit’s inquiry into the permissible amount of disgorgement sheds little light on the threshold question here: whether the SEC has shown pecuniary harm at all such that disgorgement is warranted. In Ahmed, which involved the defendant’s knowing misappropriation of millions of dollars from his employer, there was no question that the employer had suffered an economic loss. See id. at 390–91. Here, pecuniary harm is far less apparent. Because binding Circuit precedent precludes disgorgement based on the facts of this case, the SEC’s request for disgorgement and prejudgment interest[9] is DENIED. 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 retrieved on August 07, 2024, dropbox.com 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. [6] Six months after Liu, “Congress enacted § 78u(d)(7), which “gives the SEC the power to ‘seek’ and federal courts the power to ‘order’ the remedy of ‘disgorgement.’” SEC v. Govil, 86 F.4th 89, 99–100 (2d Cir. 2023) (discussing 15 U.S.C. § 78u(d)(7)). The Second Circuit has interpreted § 78u(d)(7) to limit disgorgement to the bounds established by the Supreme Court’s decision in Liu. Id. at 102 (citing SEC v. Ahmed, 72 F.4th 379, 396 (2d Cir. 2023)). But see SEC v. Hallam, 42 F.4th 316, 338 (5th Cir. 2022) (holding that “Section 78u(d) authorizes disgorgement in a legal—not equitable—sense.”). [7] Ripple separately moves to strike Fox’s declaration and supporting exhibits, arguing that they were filed late. ECF No. 951; see ECF Nos. 959, 960. In light of the Court’s finding that no disgorgement is permitted, the motion is DENIED as moot. [8] The SEC’s sole authority to the contrary, SEC v. iFresh, Inc., No. 22 Civ. 3200, 2024 WL 416709 (E.D.N.Y. Feb. 5, 2024), is an unpublished district court opinion that found (without analysis) the fact that stock prices were artificially inflated sufficient to establish pecuniary harm. Id. at *3; see SEC Reply at 9, ECF No. 961. That case— involving a consent order—required the district court to accept the SEC’s allegations of stock price inflation as true. iFresh, Inc., 2024 WL 416709 at *3. [9] “[T]he amount on which a violator must pay prejudgment interest usually tracks the amount that the party is ordered to disgorge.” Contorinis, 743 F.3d at 308. SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 3 of 5. SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 3 of 5. HackerNoon’s Legal PDF Series here II. Disgorgement A. Legal Standard A. Legal Standard In addition to injunctive relief, a district court may also order disgorgement, which “serves to remedy securities law violations by depriving violators of the fruits of their illegal conduct.” SEC v. Contorinis, 743 F.3d 296, 301 (2d Cir. 2014). Disgorgement is an equitable remedy, permissible only where it “does not exceed a wrongdoer’s net profits and is awarded for victims.” Liu v. SEC, 591 U.S. 71, 75 (2020). [6] Following Liu, the Second Circuit clarified the meaning of “victims” in SEC v. Govil, 86 F.4th 89 (2d Cir. 2023). There, the defendant caused his company “to engage in three fraudulent securities offerings,” representing to investors that the company “would use the proceeds from the transactions to satisfy outstanding debts and for general corporate purposes.” Id. at 93. “Instead, he diverted over $7.3 million of the offering proceeds to his own private accounts.” Id. The district court determined that disgorgement was warranted, reasoning that the defrauded investors were victims of the defendant’s conduct because they were lied to, even if they “may not have been financially harmed” as a result of the lie. Id. at 97. The Second Circuit reversed, holding that “a ‘victim’ for purposes of § 78u(d)(5) is one who suffers pecuniary harm from the securities fraud.” Id. at 102. The Circuit explained that an equitable remedy is meant to “restore[] the status quo”—in the context of disgorgement, by “returning the funds to victims.” Id. at 103 (quoting Liu, 591 U.S. at 80, 88) (cleaned up). Allowing “defrauded investors who suffered no pecuniary harm . . . to receive the proceeds of disgorgement,” the Circuit reasoned, would “confer[] a windfall on those who received the benefit of the bargain.” Id. at 103. Therefore, for purposes of ordering disgorgement, it is not enough that investors are lied to and “thus denied the right to make an informed decision when considering whether to make [an] investment.” Id. at 105. “[O]ffending that right” does not result in pecuniary harm.” Id. (citing Ciminelli v. United States, 598 U.S. 306, 315 (2023)) B. Application B. Application The parties dispute whether the Circuit’s decision in Govil bars disgorgement of Ripple’s profits from Institutional Sales. The Court finds that it does. The SEC contends that ordering disgorgement would not run afoul of Govil because Ripple’s violation of Section 5, via “entirely tainted transactions, caused pecuniary harm to investors.” SEC Mem. at 17. Specifically, the SEC argues that Ripple sold XRP to certain Institutional Buyers at “deep undisclosed discounts” as high as [redacted]. Id. at 18. Because Ripple failed to publicly register these discounted Institutional Sales, the SEC argues, “non-favored investors” were deprived of “the information they needed to determine whether Ripple was treating them worse and whether they could purchase [] XRP at a better price.” Id. The SEC estimates that if Ripple had disclosed its “significant discounts to other investors,” non-favored investors could have saved “potentially more than [redacted].” Id. at 19–20. The SEC further contends that the discounted sales caused pecuniary harm by “put[ting] downward pressure on the market price of XRP,” as investors who bought discounted XRP could sell for below-market prices and still profit. Id. at 20. Govil forecloses this argument. The SEC contends that Ripple “appears to have not uniformly disclosed the disparate prices and discounts offered” to Institutional Buyers. Id. at 18. The Circuit held, however, that “the right to make an informed decision when considering whether to make [an] investment” is not a property interest that can be vindicated through disgorgement. Govil, 86 F.4th at 105. As Ripple argues, many of the Govil investors would have likely been dissuaded from investing—or, at least, valued the shares at a lower price—had they known that the company’s founder intended to pocket the funds for his own use. See Ripple Opp. at 20. Yet the Circuit found that the pecuniary-harm requirement was not satisfied, as the district court acknowledged that “the investors received the return on the investment contemplated at the outset.” Govil, 86 F.4th at 105. Here, the SEC offers only speculative evidence that the Institutional Buyers did not “receive[] the return on the investment contemplated.” Id; see also id. at 104 n.16 (noting that “whether the investors actually suffered pecuniary harm would depend on the type of securities held, the terms of those securities, and when those securities were sold,” a level of detail missing from the record in Govil). The SEC’s [redacted] estimate of lost potential savings comes from an analysis conducted by SEC accountant Andrea Fox, who calculated “how much less Institutional Buyers in the aggregate would have paid for XRP if the best price offered to Institutional Buyers had been obtained by every Institutional Buyer.”[7] Fox Decl. ¶ 34, ECF No. 946. But, the SEC does not (and cannot) establish that Ripple would have, in fact, offered any additional discounts to investors had it complied with Section 5’s registration requirements. It is possible that some investors “could have demanded and potentially paid lower prices.” SEC Mem. at 18. Yet, it is at least equally possible that Ripple would have opted to offer fewer and smaller discounts overall. Cf. id. at 17 (explaining that “unregistered shares . . . are [generally] worth less” than their registered counterparts). The fact that some investors “may have been able to negotiate better deals if discounts [offered to others] were disclosed as required,” SEC Mem. at 19, does not establish that they suffered pecuniary harm by paying the sticker price. Cf. Dura Pharms., Inc. v. Broudo, 544 U.S. 336, 342 (2005) (noting that in fraud-on-the-market cases, “an inflated purchase price will not itself constitute or proximately cause the relevant economic loss”).[8] Relying on SEC v. Ahmed, 72 F.4th 379 (2d Cir. 2023), the SEC also argues that disgorgement is appropriate because Ripple’s failure to disclose the discounts “tainted” each of the Institutional Sales. SEC Reply at 9, ECF No. 961. In Ahmed, the investment-manager defendant “stole over $65 million” from his employer and ten portfolio companies, including by negotiating transactions in which he had an undisclosed conflict of interest. 72 F.4th at 390. The Second Circuit found that the conflicted transactions were “entirely tainted,” holding that the defendant was obligated to disgorge the full amount he received. Id. at 397. But the Circuit’s inquiry into the permissible amount of disgorgement sheds little light on the threshold question here: whether the SEC has shown pecuniary harm at all such that disgorgement is warranted. In Ahmed, which involved the defendant’s knowing misappropriation of millions of dollars from his employer, there was no question that the employer had suffered an economic loss. See id. at 390–91. Here, pecuniary harm is far less apparent. Because binding Circuit precedent precludes disgorgement based on the facts of this case, the SEC’s request for disgorgement and prejudgment interest[9] is DENIED. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 07, 2024, dropbox.com 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. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 07, 2024, dropbox.com 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. dropbox.com [6] Six months after Liu, “Congress enacted § 78u(d)(7), which “gives the SEC the power to ‘seek’ and federal courts the power to ‘order’ the remedy of ‘disgorgement.’” SEC v. Govil, 86 F.4th 89, 99–100 (2d Cir. 2023) (discussing 15 U.S.C. § 78u(d)(7)). The Second Circuit has interpreted § 78u(d)(7) to limit disgorgement to the bounds established by the Supreme Court’s decision in Liu. Id. at 102 (citing SEC v. Ahmed, 72 F.4th 379, 396 (2d Cir. 2023)). But see SEC v. Hallam, 42 F.4th 316, 338 (5th Cir. 2022) (holding that “Section 78u(d) authorizes disgorgement in a legal—not equitable—sense.”). [7] Ripple separately moves to strike Fox’s declaration and supporting exhibits, arguing that they were filed late. ECF No. 951; see ECF Nos. 959, 960. In light of the Court’s finding that no disgorgement is permitted, the motion is DENIED as moot. [8] The SEC’s sole authority to the contrary, SEC v. iFresh, Inc., No. 22 Civ. 3200, 2024 WL 416709 (E.D.N.Y. Feb. 5, 2024), is an unpublished district court opinion that found (without analysis) the fact that stock prices were artificially inflated sufficient to establish pecuniary harm. Id. at *3; see SEC Reply at 9, ECF No. 961. That case— involving a consent order—required the district court to accept the SEC’s allegations of stock price inflation as true. iFresh, Inc., 2024 WL 416709 at *3. [9] “[T]he amount on which a violator must pay prejudgment interest usually tracks the amount that the party is ordered to disgorge.” Contorinis, 743 F.3d at 308.

Court Denies SEC's Request for $1 Billion in Disgorgement from Ripple

OpenTelemetry Tracing in Spring Boot: Choosing Between Java Agent and Micrometer Tracing

স্প্রিং বুটে ওপেনটেলিমেট্রি ট্রেসিং: এর জন্য জাভা এজেন্ট এবং মাইক্রোমিটারের মধ্যে নির্বাচন করা 

OpenTelemetry Tracing in Spring Boot: Auswahl zwischen Java Agent und Micrometer für

Seguimiento de OpenTelemetry en Spring Boot: elección entre Java Agent y Micrometer para

Traçage OpenTelemetry dans Spring Boot : choisir entre Java Agent et Micrometer pour

स्प्रिंग बूट में ओपनटेलीमेट्री ट्रेसिंग: जावा एजेंट और माइक्रोमीटर के बीच चयन करना

Spring BootでのOpenTelemetryトレース: JavaエージェントとMicrometerの選択

Spring Boot에서 OpenTelemetry 추적: Java Agent와 Micrometer 중에서 선택

Rastreamento OpenTelemetry no Spring Boot: Escolhendo entre Java Agent e Micrômetro para

Трассировка OpenTelemetry в Spring Boot: выбор между Java Agent и Micrometer для

Spring Boot'ta OpenTelemetry İzleme: Java Agent ve Micrometer Arasında Seçim Yapmak

OpenTelemetry Tracing trong Spring Boot: Lựa chọn giữa Java Agent và Micrometer cho

Spring Boot 中的 OpenTelemetry 跟踪：在 Java Agent 和 Micrometer 之间进行选择

It can appear that the world at large is facing a litany of irresolvable issues. Cost of living crises, natural disasters, 40-year inflation highs, huge interest rates, unaffordable rent, the Ukraine war, AI-based employment fears, privacy intrusions, viruses, increasing disease rates, declining social security funds, online censorship, and more. Yet all of this can be easily simplified into ONE, critical issue: FINANCE. It is through finance that the government and other actors are empowered to make (secret) decisions that are clearly detrimental to those who were fooled into supporting them. Money printing is the source of problems, and printed money is mainly used to buy government treasury bonds, propping up a fraudulent system. Closely tied in with financial control are KYC procedures, which have proven increasingly invasive and with very little justification. There is no intrinsic reason that sensitive information should be uploaded just for making payments, as this treats people like criminals before any suggestion of wrongdoing. Bitcoin swiftly addresses all of this. A Brief BTC History On the Bitcoin Genesis Block were imprinted the words “The Times 03/Jan/2009. Chancellor on brink of second bailout for banks.”. This was a clear reference to the fact that Bitcoin was needed as an alternative to the fiat system. Satoshi Nakamoto laid the groundwork for an ongoing financial revolution with a very coherent message. Since 2009, little has changed. If anything, the use case for Bitcoin is even more relevant, and central banks have increased their powers.  We are still in a position where we need a globally recognized alternative to printed fiat money to help world citizens. I previously wrote about the importance of a global constitution and one world nation, which is the only viable path forward. In the meantime, the MSM spent a solid 12+ years making up false narratives about Bitcoin. They said it was a Ponzi scheme. They said it used up too much energy. They said it would die off within a few years. They said it was used to launder money by ‘terrorists’, an often used and ill-defined term. And they never retracted a single one of their statements. They consistently made such statements while propping up a system with remarkable similarities to a Ponzi scheme, which is used to launch wars and surveillance operations, through a highly fraudulent banking system that has a proven track record of billion-dollar money laundering. Why Bitcoin For Planetary Sovereignty? Aside from attacks that were designed specifically to discredit Bitcoin on false premises, there were, at one time, legitimate concerns. Many cited the fact that Bitcoin’s PoW consensus mechanism was slow, high-cost, and inefficient. Yet all these concerns have been dealt with through numerous L2s such as Rootstock, Stacks, and Lightning Network. Rootstock

Stacks

Lightning Network



EVM Compatible

Yes

No

No



Micro Payments

Yes

Yes

Yes



Type

Ethereum Sidechain

Independent Blockchain

State Channel



Mining

Merged Mining

PoX

NA



Native Token

RBTC

STX

NA With the possible exception of Monero, Bitcoin is the only cryptocurrency that is truly decentralized in terms of its servers and its founders. Nobody knows who founded Bitcoin and there has even been a server launched into outer space. It has the highest adoption rates and is the most well-known. Of course, it also has the largest market cap and trade volume. Bitcoin is one of the few crypto movements that can be said to be purely grassroots and people-powered, with no initial investment, no marketing, and no vested interests. It’s why world governments and central banks hate it so much - there are no founders or investors to harass and target. It’s a truly anti-fragile technology that gets stronger the more it is attacked. Bitcoin Adoption Levels Bitcoin adoption levels are through the roof and have been helped hugely by irresponsible government spending, lockdowns, privacy intrusions, and frustrating social policies. It is becoming popular both in countries with large numbers of unbanked as well as in developed countries with draconian regulatory procedures. Remember, we are still in the early days of Bitcoin adoption and it has much more room for growth. The globe at large is slowly, if reluctantly, waking up to the fact that fiat-based governments act in a way that is indistinguishable from the threats they say they are protecting us from. Thankfully, this trend of alertness is accelerating and pushing people towards Bitcoin. The following are some inspiring adoption facts relevant to Bitcoin: The government of El Salvador has adopted Bitcoin as a legal tender and made huge profits doing so, despite much criticism. The Central African Republic has done the same. Small towns in South Africa have integrated Bitcoin as an accepted means of payment. Other areas include Bitcoin Beach in El Salvador.


Special Economic Zones (SEVs) such as Prospera in Honduras have a 5% tax rate and are based on decentralization and bitcoin-based capital.


Social media sites like Substack and Twitter (X) have integrated Bitcoin’s Lightning Network as a means of payment. Paxful also accepts Lightning Network payments, as does McDonalds’ and Walmart. In terms of on-chain metrics, active wallet addresses are nearing all-time highs, and the price is widely expected to hit $100,000 by 2025. A Bitcoin ETF was recently approved by the SEC. At the time of writing, there are over 32,000 Bitcoin ATMs in the US alone. Attitude is Everything An uplifting idiom that is often heard among BTC proponents is to “stay humble, stack satoshis”. This is the needed attitude to have, not mindless profit-taking driven by 100x returns and a mindless meme frenzy. It’s about financial equality and the creation of a new economy built on trust, transparency, and liberty, the polar opposite of the existing fiat ecosystem. With L2s like Rootstock, LW, and Stacks rendering moot most prior criticisms of Bitcoin, we are all set to take back the financial economy through the only crypto that has ever mattered: Bitcoin Business owners should be encouraged, across the world, to do their part and accept BTC for payment. Not as a means of taking profits on Bitcoin holding, but so it becomes an accepted norm for payment transactions. This will lead to liberated financial markets, known as KYC-free Bitcoin Circular Economies. Despite decades of propaganda, the truth is that Bitcoin is the PERFECT solution to a broken financial system and the only thing needed is usage and adoption. It’s low-cost, scalable, fast, and iconic. It’s also more than capable of handling large transaction volumes and will help to alleviate a world under siege by inept politicians and corrupt central banks. The rate of Bitcoin adoption across the globe might be the best metric we have to measure individual liberties. It can appear that the world at large is facing a litany of irresolvable issues. Cost of living crises, natural disasters, 40-year inflation highs , huge interest rates, unaffordable rent, the Ukraine war, AI-based employment fears, privacy intrusions, viruses, increasing disease rates, declining social security funds, online censorship, and more. 40-year inflation highs Yet all of this can be easily simplified into ONE, critical issue: FINANCE. It is through finance that the government and other actors are empowered to make (secret) decisions that are clearly detrimental to those who were fooled into supporting them. Money printing is the source of problems, and printed money is mainly used to buy government treasury bonds, propping up a fraudulent system. Closely tied in with financial control are KYC procedures, which have proven increasingly invasive and with very little justification. There is no intrinsic reason that sensitive information should be uploaded just for making payments, as this treats people like criminals before any suggestion of wrongdoing. Bitcoin swiftly addresses all of this. Closely tied in with financial control are KYC procedures, which have proven increasingly invasive and with very little justification. There is no intrinsic reason that sensitive information should be uploaded just for making payments, as this treats people like criminals before any suggestion of wrongdoing. Bitcoin swiftly addresses all of this. A Brief BTC History A Brief BTC History On the Bitcoin Genesis Block were imprinted the words “The Times 03/Jan/2009. Chancellor on brink of second bailout for banks.”. This was a clear reference to the fact that Bitcoin was needed as an alternative to the fiat system. Satoshi Nakamoto laid the groundwork for an ongoing financial revolution with a very coherent message. “The Times 03/Jan/2009. Chancellor on brink of second bailout for banks.”. Since 2009, little has changed. If anything, the use case for Bitcoin is even more relevant, and central banks have increased their powers.  We are still in a position where we need a globally recognized alternative to printed fiat money to help world citizens. I previously wrote about the importance of a global constitution and one world nation, which is the only viable path forward. Bitcoin the importance of a global constitution In the meantime, the MSM spent a solid 12+ years making up false narratives about Bitcoin. They said it was a Ponzi scheme. They said it used up too much energy. They said it would die off within a few years. They said it was used to launder money by ‘terrorists’, an often used and ill-defined term. And they never retracted a single one of their statements. a single one They consistently made such statements while propping up a system with remarkable similarities to a Ponzi scheme, which is used to launch wars and surveillance operations, through a highly fraudulent banking system that has a proven track record of billion-dollar money laundering. proven billion-dollar money laundering. Why Bitcoin For Planetary Sovereignty? Why Bitcoin For Planetary Sovereignty? Aside from attacks that were designed specifically to discredit Bitcoin on false premises, there were, at one time, legitimate concerns. Many cited the fact that Bitcoin’s PoW consensus mechanism was slow, high-cost, and inefficient. Yet all these concerns have been dealt with through numerous L2s such as Rootstock, Stacks, and Lightning Network. Rootstock

Stacks

Lightning Network



EVM Compatible

Yes

No

No



Micro Payments

Yes

Yes

Yes



Type

Ethereum Sidechain

Independent Blockchain

State Channel



Mining

Merged Mining

PoX

NA



Native Token

RBTC

STX

NA Rootstock

Stacks

Lightning Network EVM Compatible

Yes

No

No Micro Payments

Yes

Yes

Yes Type

Ethereum Sidechain

Independent Blockchain

State Channel Mining

Merged Mining

PoX

NA Native Token

RBTC

STX

NA Rootstock Stacks Lightning Network Rootstock Rootstock Stacks Stacks Lightning Network Lightning Network EVM Compatible Yes No No EVM Compatible EVM Compatible Yes Yes No No No No Micro Payments Yes Yes Yes Micro Payments Micro Payments Yes Yes Yes Yes Yes Yes Type Ethereum Sidechain Independent Blockchain State Channel Type Type Ethereum Sidechain Ethereum Sidechain Independent Blockchain Independent Blockchain State Channel State Channel Mining Merged Mining PoX NA Mining Mining Merged Mining Merged Mining PoX PoX NA NA Native Token RBTC STX NA Native Token Native Token RBTC RBTC STX STX NA NA With the possible exception of Monero, Bitcoin is the only cryptocurrency that is truly decentralized in terms of its servers and its founders. Nobody knows who founded Bitcoin and there has even been a server launched into outer space. It has the highest adoption rates and is the most well-known. Of course, it also has the largest market cap and trade volume. Bitcoin is one of the few crypto movements that can be said to be purely grassroots and people-powered, with no initial investment, no marketing, and no vested interests. It’s why world governments and central banks hate it so much - there are no founders or investors to harass and target. It’s a truly anti-fragile technology that gets stronger the more it is attacked. Bitcoin Adoption Levels Bitcoin Adoption Levels Bitcoin adoption levels are through the roof and have been helped hugely by irresponsible government spending, lockdowns, privacy intrusions, and frustrating social policies. It is becoming popular both in countries with large numbers of unbanked as well as in developed countries with draconian regulatory procedures. Remember, we are still in the early days of Bitcoin adoption and it has much more room for growth. The globe at large is slowly, if reluctantly, waking up to the fact that fiat-based governments act in a way that is indistinguishable from the threats they say they are protecting us from. Thankfully, this trend of alertness is accelerating and pushing people towards Bitcoin. The following are some inspiring adoption facts relevant to Bitcoin: The government of El Salvador has adopted Bitcoin as a legal tender and made huge profits doing so, despite much criticism. The Central African Republic has done the same. The government of El Salvador has adopted Bitcoin as a legal tender and made huge profits doing so, despite much criticism. The Central African Republic has done the same. Bitcoin as a legal tender Bitcoin as a legal tender Small towns in South Africa have integrated Bitcoin as an accepted means of payment. Other areas include Bitcoin Beach in El Salvador. Special Economic Zones (SEVs) such as Prospera in Honduras have a 5% tax rate and are based on decentralization and bitcoin-based capital. Social media sites like Substack and Twitter (X) have integrated Bitcoin’s Lightning Network as a means of payment. Paxful also accepts Lightning Network payments, as does McDonalds’ and Walmart. Small towns in South Africa have integrated Bitcoin as an accepted means of payment. Other areas include Bitcoin Beach in El Salvador. Small towns in South Africa have integrated Bitcoin as an accepted means of payment. Other areas include Bitcoin Beach in El Salvador. integrated Bitcoin integrated Bitcoin Special Economic Zones (SEVs) such as Prospera in Honduras have a 5% tax rate and are based on decentralization and bitcoin-based capital. Special Economic Zones (SEVs) such as Prospera in Honduras have a 5% tax rate and are based on decentralization and bitcoin-based capital. Prospera in Honduras Prospera in Honduras Social media sites like Substack and Twitter (X) have integrated Bitcoin’s Lightning Network as a means of payment. Paxful also accepts Lightning Network payments, as does McDonalds’ and Walmart. Social media sites like Substack and Twitter (X) have integrated Bitcoin’s Lightning Network as a means of payment. Paxful also accepts Lightning Network payments, as does McDonalds’ and Walmart. In terms of on-chain metrics, active wallet addresses are nearing all-time highs, and the price is widely expected to hit $100,000 by 2025 . A Bitcoin ETF was recently approved by the SEC. At the time of writing, there are over 32,000 Bitcoin ATMs in the US alone. $100,000 by 2025 $100,000 by 2025 Attitude is Everything Attitude is Everything An uplifting idiom that is often heard among BTC proponents is to “stay humble, stack satoshis” . This is the needed attitude to have, not mindless profit-taking driven by 100x returns and a mindless meme frenzy. It’s about financial equality and the creation of a new economy built on trust, transparency, and liberty, the polar opposite of the existing fiat ecosystem. BTC “stay humble, stack satoshis” With L2s like Rootstock, LW, and Stacks rendering moot most prior criticisms of Bitcoin, we are all set to take back the financial economy through the only crypto that has ever mattered: Bitcoin With L2s like Rootstock, LW, and Stacks rendering moot most prior criticisms of Bitcoin, we are all set to take back the financial economy through the only crypto that has ever mattered: Bitcoin Business owners should be encouraged, across the world, to do their part and accept BTC for payment. Not as a means of taking profits on Bitcoin holding, but so it becomes an accepted norm for payment transactions. This will lead to liberated financial markets, known as KYC-free Bitcoin Circular Economies . KYC-free Bitcoin Circular Economies KYC-free Bitcoin Circular Economies Despite decades of propaganda, the truth is that Bitcoin is the PERFECT solution to a broken financial system and the only thing needed is usage and adoption. It’s low-cost, scalable, fast, and iconic. It’s also more than capable of handling large transaction volumes and will help to alleviate a world under siege by inept politicians and corrupt central banks. The rate of Bitcoin adoption across the globe might be the best metric we have to measure individual liberties.

Bitcoin Adoption & Planetary Sovereignty - One And The Same?

বিটকয়েন গ্রহণ এবং গ্রহের সার্বভৌমত্ব - এক এবং একই?

Bitcoin-Einführung und planetare Souveränität – ein und dasselbe?

Adopción de Bitcoin y soberanía planetaria: ¿uno y el mismo?

Adoption du Bitcoin et souveraineté planétaire : une seule et même chose ?

बिटकॉइन अपनाना और ग्रहीय संप्रभुता - एक ही बात?

ビットコインの採用と惑星の主権は同じもの？

비트코인 채택과 행성 주권 - 하나이고 같은가?

Adoção do Bitcoin e Soberania Planetária - A Mesma?

Принятие биткойнов и планетарный суверенитет – одно и то же?

Bitcoin'in Benimsenmesi ve Gezegensel Egemenlik - Bir Ve Aynı mı?

Việc áp dụng Bitcoin và chủ quyền hành tinh - Một và giống nhau?

比特币的采用和行星主权——是一回事吗？

Four Thousand Years of Blood, Snot, and Tears

চার হাজার বছরের রক্ত, স্নোট এবং অশ্রু

Viertausend Jahre Blut, Rotz und Tränen

Cuatro mil años de sangre, mocos y lágrimas

Quatre mille ans de sang, de morve et de larmes

चार हज़ार साल का खून, बलगम और आँसू

4000年にわたる血と鼻水と涙

4천년의 피와 콧물과 눈물

Quatro mil anos de sangue, ranho e lágrimas

Четыре тысячи лет крови, соплей и слез

Dört Bin Yıllık Kan, Sümük ve Gözyaşı

Bốn ngàn năm máu, nước mũi và nước mắt

四千年的血、鼻、泪

Authors: (1) William P. Wagner IV, Claremont Graduate University. Table of Links Abstract & Introduction Definition of Key Terms Fundamentals of Internet Operation Encapsulation Usage-Based Economic Models Net Neutrality Legal History Researcher Conclusions Areas for Further Exploration & References 7. Legal History The basis of all legal challenges – either for or against net neutrality legislation lies in determining which agency will regulate the Internet in the U.S., and then what rules that agency will apply. 7.1. 2005 Order In 2005 the FCC released the first agency guidelines regarding the “open Internet” – the four rules [13]: “To encourage broadband deployment and preserve and promote the open and interconnected nature of the public Internet, ... • consumers are entitled to access the lawful Internet content of their choice. • consumers are entitled to run applications and use services of their choice, subject to the needs of law enforcement. • consumers are entitled to connect their choice of legal devices that do not harm the network. • consumers are entitled to competition among network providers, application and service providers, and content providers.” 7.2. National Cable & Telecommunications Association v. Brand X Internet Services (2005) These rules were challenged by industry groups representing the ISPs as being too broad. And greatly complicating the issue down the road, in Section II, Paragraph 4, the FCC specifically states that the ISPs are not Title II but are in fact covered under Title I ancillary. On June 27, 2005 the United States Supreme Court (applying Chevron deference) upheld a determination by the FCC that cable Internet providers were an "information service," and not a "telecommunications service" as classified under the Telecommunications Act of 1996. Supreme Court Justice Antonin Scalia wrote the dissenting opinion, and we will see that his words would later seem prescient when quoted in the majority opinion ten years later in 2016[15]. 7.3. 2010 Open Internet Order In a 2010 Executive Order, President Obama directed the FCC to formalize regulations regarding Net Neutrality. The 2010 Order applied much of the same oversight of “common carriers” to the ISPs. The 2010 Order laid out three basic principles: transparency, no blocking, and no unreasonable discrimination. 7.4. Verizon Communications Inc. v. FCC (2014) The 2010 Order was tested in the Supreme Court when Verizon sued the FCC. The Supreme Court ruled that two of the three main principles of the 2010 Order did not fall under the FCC’s jurisdiction of Title I ancillary. The Court upheld the Transparency requirements but found that the other two requirements required the FCC to treat ISPs as “common carriers.” In its findings, the Court suggested some possible fixes. The Court also recognized the “Virtuous Cycle” of edge providers and recognized that ISPs had the “means and the incentive” to disrupt this cycle to the detriment of consumers. 7.5. 2015 REPORT AND ORDER ON REMAND, DECLARATORY RULING, AND ORDER After a public comments period, the FCC sought to re-define the regulations to define ISPs as “common carriers” and further codify the rules regarding Net Neutrality. In February of 2015 the FCC published the REPORT AND ORDER ON REMAND, DECLARATORY RULING, AND ORDER. The 2015 Order classifies ISPs under Title II of the Communications Acts of 1934,1996[14] and it gives strict guidelines regarding Net Neutrality. The 2015 Order states no blocking, no throttling, and no paid prioritization. The FCC sited the 2014 Verizon findings in the order and pointed to the Court’s reference to the “Virtuous Cycle” as authority to change the ISP classification to “common carrier.” 7.6. United States Telecom Association v. FCC and Independent Telephone and Telecommunications Alliance (2016). The large telecom companies sued the FCC, however this time the smaller telecoms joined on the side of the FCC. On June 14 of 2016, the U.S. Court of Appeals for the District of Columbia refused to hear the case, thereby upholding the ruling in USTA v FCC that the FCC had operated under its authority to re-classify the ISPs as “common carriers.” Circuit Judge Srinivasan joined by Circuit Judge Tatel wrote the majority opinion. Amongst the various considerations was the following [15]: Justice Scalia’s dissenting opinion [in the 2005 decision], joined by Justices Souter and Ginsburg, went even further. According to Justice Scalia, the statute permitted only one conclusion: cable broadband ISPs “are subject to Title II regulation as common carriers, like their chief competitors [e.g., DSL] who provide Internet access through other technologies.” Id. at 1006 (Scalia, J., dissenting). The agency, in Justice Scalia’s view, had no discretion to conclude otherwise. And he expressly accepted that his reading of the Act would result in “common carrier regulation of all ISPs,” a result he considered “not a worry.” Id. At 1011. The large telecoms appealed to the U.S. Supreme Court and on November 5th, 2018 the U.S. Supreme Court refused to hear the case, thereby upholding the previous findings. 7.7. 2017 - 2018 FCC Changes Direction In 2017 President Donald Trump appointed Ajit Pai as Chairman of the FCC. Chairman Pai has changed the FCC’s direction and decided the FCC should not regulate ISPs with regard to Net Neutrality, thereby rendering the Court’s ruling temporarily moot as it applies to the current direction of the FCC. The FCC did stipulate that ISPs must be transparent in any throttling or associated fees. The Supreme Court’s 2018 finding does however mean that should the FCC change direction again, that the stricter rules would have legal precedence. It may also be cited as precedence in any upcoming legal challenges to the current interpretation. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

The Evolution of Net Neutrality Laws

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References IV. SUPPORTED EXPERIMENTATION This section delves into the features of the testbed that users can customize to meet their unique experimental goals. These features can be broadly categorized into two types: (1) configurations related to workload and (2) configurations related to the network. In terms of workload-related configurations, ExPECA offers researchers a range of options to tailor to their experiments. Users can select from various geographical locations for computational workloads using the cloud interface and by reserving public IPs. It is possible to integrate GPUs for tasks requiring accelerated processing, or choose a CPU with a certain number of cores, and allocate a specific amount of RAM to benchmark workload performance. The platform supports experiments involving both static (fixed-location) and mobile (varying-location, e.g., drone with a wireless connectivity dongle) wireless end nodes. For network-related configurations, ExPECA provides a diverse array of network types, such as SDR 5G, SDR LTE, SDR WiFi, and COTS 5G, among others. Researchers can also customize network topologies and control interference levels, thanks to the testbed’s isolated location. The platform supports a wide range of wireless protocols compatible with USRP e320 and allows users to configure channel conditions. Additionally, the use of Software Defined Radios (SDRs) offers further flexibility, including control over transmit power and the ability to implement a variety of wireless protocols. ExPECA offers the opportunity to explore various experimentation scenarios involving edge computing, wireless communication, and machine learning research. Here we explain various scenarios, each shedding light on some of the interesting aspects of wireless communication and/or edge computing offering unique insights into the dynamic landscape of edge computing. In Figure 6, we provide an illustration that helps a reader to visualize these scenarios. Below, we provide a few of such use cases in various research areas. While some of these aim at direct measurements for data-driven modeling and prediction, others target initial observations for forthcoming theoretical work or the verification and validation of existing theoretical models. A. Optimization of Closed-Loop Applications With the booming interest and applications surrounding cyber-physical systems, it is imperative to study Networked Control Systems (NCSs) [8]. NCSs form crucial control systems where the control loops are closed through a communication network. Due to the safety-critical nature of the applications these NCSs encompass, such as robots, UAVs etc., there is a need to benchmark and understand the bottlenecks/boundaries associated with these applications [9]. Namely, the latency and reliability boundaries of robotic control are crucial to understand before they can be deployed in real-world scenarios, where their potential failure might prove expensive [10]. Another such feedback system where latency is of importance are human-in-the-loop applications such as augmented reality, wearable cognitive assistants (WCA) [9], [11], and ambient safety. In the context of criticality, we have systems of automated fault detection, in which case the acoustic [12] or motion amplified visual [13], [14] data is processed for vibration analysis to potentially trigger some maintenance, safety or emergency procedures [12]. The ExPECA testbed provides developers with the opportunities to experiment with workloads corresponding to these types of applications in a containerized manner with various real network architectures. These experiments could either be for data collection or for verification of existing theoretical models. B. Time-sensitive Wireless Networking Supporting the above-mentioned CPS and HITL applications requires the network to offer a certain level of endto-end network latency, often combined with extremely high reliability, in order to operate correctly and safely. This has spurred various efforts to integrate wireless networking technologies (e.g., 5G URLLC) with wired technologies (e.g., TSN) as shown in Figure 7. However, an efficient wiredwireless integration realization to support time-critical applications necessitates accurate system characterization. In particular, latency characterization of wireless segments is crucial as they are subject to various stochastic and temporal influences, unlike wired links where latencies are immune to external influences. Furthermore, accurate predictions of wireless latency are also envisioned to be leveraged in 6G networks to support deterministic communications [15]. Lately, several data-driven methods for latency prediction have been proposed [16], [17]. Training and validating these approaches require a vast amount of datasets comprising latency measurements alongside network parameters (e.g., SINR and MCS). Precise time-synchronization of all nodes of the testbed ensures accurate latency measurements can be collected between any two nodes of the testbed. These experiments can employ diverse wireless communication technologies, including COTS 5G, software-defined 5G and IEEE 802.11g Wi-Fi integrated in ExPECA. Furthermore, the containerized workflow within ExPECA enables users to perform automated, reproducible and repetitive latency measurement experiments. C. Hierarchical Learning and Inference Another set of experiments researchers can use the ExPECA testbed for is the verification of theoretical results corresponding to Hierarchical Learning (HI) [18], [19]. In these works, authors develop the idea of Hierarchical Inference, where a compute constrained edge device chooses to perform local inference or classification of an image sample, or offloading via a wireless channel to get help of a high power edge server for classification. A small-sized ML model with limited accuracy is assumed to have been deployed at the edge device and offloading to the server incurs cost. In essence, these policies aim to make intelligent decisions regarding task offloading, prioritizing complex tasks or tasks where accuracy is paramount. Conversely, they process simpler tasks or tasks where reduced latency is of utmost importance locally. It is to be noted that in this specific work on Hierarchical Inference this decision is made in an online manner. Using the ExPECA testbed, researchers can plan to implement the various algorithms proposed and verify if the real-world results match simulations and models. D. Federated Learning Validation Federated learning (FL) is emerging as one of the key approaches for training machine learning models [20]. FL aims to improve over centralized learning in terms of privacy, total training time, computation overhead, etc. However, FL can cause large communication overhead because the machine learning model should be repeatedly exchanged between the central server and the participating nodes during the training process. In order to analyze and optimize communication overhead, many works have proposed models for FL systems operating in wireless environments [21], [22]. Validating these theoretical models in real systems is required to reveal otherwise unavailable insights. The ExPECA testbed supports experimentation involving both centralized learning (CL) and federated learning to evaluate them in terms of communication/computation overhead and accuracy with various wireless conditions. For example in case of communication overhead, CL and FL will consume communication resources when exchanging data (i.e., using uplink to send input data in CL and uplink/downlink to send machine learning model in FL). The ExPECA testbed is able to measure the communication resource usage, and training time (communication/computation time), and capture wireless parameters at that time. Thanks to improved reproducibility and repeatability, the ExPECA testbed supplies stable validation for machine learning architectures.

ExPECA: An Experimental Platform for Trustworthy: Supported Experimentatation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References V. ACKNOWLEDGEMENTS This research has been partially funded by (1) the VINNOVA Competence Center for Trustworthy Edge Computing Systems and Applications (TECoSA) at KTH Royal Institute of Technology; and (2) the Swedish Foundation for Strategic Research (SSF), through grant number ITM17–0246. REFERENCES [1] D. Raychaudhuri, I. Seskar, G. Zussman, T. Korakis, D. Kilper, T. Chen, J. Kolodziejski, M. Sherman, Z. Kostic, X. Gu, H. Krishnaswamy, S. Maheshwari, P. Skrimponis, and C. Gutterman, Challenge: COSMOS: A City-Scale Programmable Testbed for Experimentation with Advanced Wireless. New York, NY, USA: Association for Computing Machinery, 2020. [2] J. Yu, T. Chen, C. Gutterman, S. Zhu, G. Zussman, I. Seskar, and D. Kilper, “Cosmos: Optical architecture and prototyping,” in 2019 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1– 3, 2019. [3] J. Breen, A. Buffmire, J. Duerig, K. Dutt, E. Eide, M. Hibler, D. Johnson, S. K. Kasera, E. Lewis, D. Maas, A. Orange, N. Patwari, D. Reading, R. Ricci, D. Schurig, L. B. Stoller, J. Van der Merwe, K. Webb, and G. Wong, “POWDER: Platform for open wireless data-driven experimental research,” in Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH), Sept. 2020. [4] H. Zhang, Y. Guan, A. Kamal, D. Qiao, M. Zheng, A. Arora, O. Boyraz, B. Cox, T. Daniels, M. Darr, et al., “Ara: A wireless living lab vision for smart and connected rural communities,” in Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization, pp. 9–16, 2022. [5] K. Keahey, J. Anderson, Z. Zhen, P. Riteau, P. Ruth, D. Stanzione, M. Cevik, J. Colleran, H. S. Gunawi, C. Hammock, J. Mambretti, A. Barnes, F. Halbach, A. Rocha, and J. Stubbs, “Lessons learned from the Chameleon Testbed,” in Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), USENIX Association, July 2020. [6] K. Keahey, J. Anderson, M. Sherman, C. Hammock, Z. Zhen, J. Tillotson, T. Bargo, L. Long, T. Ul Islam, S. Babu, H. Zhang, and F. Halbach, “Chi-in-a-box: Reducing operational costs of research testbeds,” in Practice and Experience in Advanced Research Computing, PEARC ’22, (New York, NY, USA), Association for Computing Machinery, 2022. [7] S. George, T. Eiszler, R. Iyengar, H. Turki, Z. Feng, J. Wang, P. Pillai, and M. Satyanarayanan, “Openrtist: End-to-end benchmarking for edge computing,” IEEE Pervasive Computing, vol. 19, no. 4, pp. 10–18, 2020. [8] X.-M. Zhang, Q.-L. Han, X. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: a survey of trends and techniques,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 1–17, 2020. [9] M. Olgu´ın Munoz, ˜ An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications. PhD thesis, KTH Royal Institute of Technology, 2023. [10] M. Olgu´ın Munoz, N. Roy, and J. Gross, “CLEAVE: Scalable and edge- ˜ native benchmarking of networked control systems,” in Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’22, (New York, NY, USA), pp. 37–42, Association for Computing Machinery, 2022. [11] K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” in Proc. of the 12th Annual International Conference on Mobile Systems, Applications, and Services, p. 68–81, Association for Computing Machinery, 2014. [12] B. Lu, Y. Li, X. Wu, and Z. Yang, “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” in IEEE Power Electronics and Machines in Wind Applications, pp. 1–7, 2009. [13] T.-H. Oh, R. Jaroensri, C. Kim, M. Elgharib, F. Durand, W. T. Freeman, and W. Matusik, “Learning-based video motion magnification,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 633–648, 2018. [14] C. Liu, A. Torralba, W. T. 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Gross, “Online algorithms for hierarchical inference in deep learning applications at the edge,” 2023. [19] G. Al-Atat, A. Fresa, A. P. Behera, V. N. Moothedath, J. Gross, and J. P. Champati, “The case for hierarchical deep learning inference at the network edge,” in Proceedings of the 1st International Workshop on Networked AI Systems, NetAISys ’23, (New York, NY, USA), Association for Computing Machinery, 2023. [20] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54 of Proceedings of Machine Learning Research, pp. 1273–1282, PMLR, 20–22 Apr 2017. [21] H. Gao, A. Xu, and H. Huang, “On the Convergence of CommunicationEfficient Local SGD for Federated Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7510–7518, May 2021. [22] J. Perazzone, S. Wang, M. 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ExPECA: An Experimental Platform for Trustworthy: Acknowledgements & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References III. EXPERIMENTAL TESTBED VALIDATION This section aims to substantiate two key assertions about ExPECA: its efficiency in facilitating end-to-end experiments and its ability to ensure experimental reproducibility[1]. To validate the first claim, we employ OpenRTiST [7], an open-source application that is computationally demanding, bandwidth-intensive, and latency-sensitive. OpenRTiST uses machine learning filtering to overlay various artistic styles onto video frames, creating an augmented reality experience. In this experiment, the OpenRTiST client connects to the server either at the edge, specifically the testbed servers, or in the cloud via a server located at Carnegie Mellon University (CMU), in Pittsburgh, Pennsylvania, through a COTS 5G system and the adv-03 node. In Figure 4, we depict the corresponding critical performance indicators such as frames per second (FPS) and round-trip time (RTT) measured using the testbed. In Figure 4a, we plot the Cumulative Distribution Function (CDF) of FPS, and in Figure 4a we plot the Complementary CDF (CCDF) of RTT. As we move from edge to cloud, RTT significantly increases which results in lower refresh rates and less responsive video stream. The performance is benchmarked against different video frame sizes to analyze network and compute capacity. For the second claim, we concentrate on wireless network latency, a metric inherently challenging to model due to its stochastic nature at the packet level. While latency can be influenced by factors like transmit power and channel interference, it also depends on wireless protocol settings such as modulation schemes, bandwidth, and antenna configurations. ExPECA’s isolated location minimizes variability in channel quality, mitigating potential interference. In Figure 5, we present latency measurements across different radio links over several days, demonstrating the reproducibility level in the case of COTS 5G wireless links. The close similarity between these CCDFs confirms the reproducibility of wireless latency measurements achieved through ExPECA. [1] Experiments available as Python Jupyter notebooks at https://github.com/KTH-EXPECA/examples

ExPECA: An Experimental Platform for Trustworthy: Experimental Testbed Validation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References II. TESTBED DESIGN AND ARCHITECTURE The ExPECA testbed is designed to address two primary challenges pervasive in edge computing research: 1) endto-end experimentation, 2) ensuring repeatability and reproducibility. To enable end-to-end edge computing experiments that encompass both communication and computation aspects, ExPECA is engineered with the following primary goals: Provision communication components by utilizing a diverse array of wireless and wired links. 2) Executing and overseeing computational elements within containerized environments running on the testbed infrastructure. These objectives are realized through an architectural framework depicted in Figure 1. Central to this architecture is a versatile network fabric that ensures IP connectivity among different experimental components, which may include servers located in the cloud or at the edge, as well as wireless terminals. Distinctive design choices have been made to realize these objectives, distinguishing ExPECA from contemporary solutions. We here describe these choices and their impacts. A. Location and Physical Environment The testbed is located in KTH R1 hall, an experimental facility 25 meters below ground. This isolated location offers a controlled environment with minimal radio interference, making it ideal for running reproducible wireless experiments. With dimensions of 12 meters in width, 24 meters in length, and 12 meters in height, the facility is spacious enough to accommodate a variety of remotely controlled devices, such as drones and cars, providing researchers with considerable freedom in designing their experiments. B. Control and Management Software We adopted CHI-in-a-Box as a baseline testbed implementation with extensions and contributions from our team. CHI-in-a-Box is a packaging of the CHameleon Infrastructure (CHI) software framework which is built primarily on top of the mainstream open-source OpenStack platform. CHI has mature support for edge-to-cloud provisioning of compute, storage, and network fabrics, and provides end-to-end experiment control and user services. Specifically, we leverage the CHI@Edge flavor of the framework, a novel version that supports running containerized workloads on compute resources. In the following, we describe the essential elements of CHI. Users have the flexibility to allocate resources either on-the-fly or through advance reservations, facilitated by customized versions of OpenStack’s Blazar and Doni services. These services have been tailored by the ExPECA team to include unique features such as radio and Kubernetes node reservations. The scope of allocatable resources encompasses worker nodes, radios, networks, and IP addresses. Once resources are secured, the integrated Kubernetes orchestration system enables the deployment of containerized workloads via OpenStack Zun. Users have the option to utilize preconfigured Docker images supplied by the ExPECA team (as in the case of SDR RAN) or to upload their own container images. Notably, the ExPECA team has extensively modified Zun to support functionalities like network interface and block storage attachments to K8S containers, features not originally supported in CHI@Edge. Configuration of complex experiment topologies such as distributed networking experiments is supported through CHI’s programmable interface and our adopted flavor of the python-chi Python library. In addition to these, OpenStack Neutron plays a pivotal role in managing the network architecture. Neutron provides a robust set of networking capabilities, including virtual networking, that allows for the creation of isolated networks, subnets, and routers. We connect all testbed components, including radios, to managed Layer 2 switches controlled by Neutron. C. Radio Nodes One of the standout features of ExPECA is the inclusion of wireless terminals as reservable resources. These radios are mounted at diverse locations in R1, at three different levels, as shown in Figure 3. They are categorized into two types: SDR Nodes: These can serve as the air interface for any wireless protocol implemented for USRP E320 SDRs, including OpenAirInterface (OAI) 5G, OAI LTE, SRS LTE, Mangocomm IEEE802.11b/g, or any GNU radiobased wireless protocol. 2) COTS Radios: Includes an Ericsson private 5G system with radio dots and Advantech routers serving as 4G/5G COTS UE nodes. We chose all radios, specifically SDRs, to be equipped with IP-networked interfaces. This allowed us to enroll them as network segments in Openstack Blazar and Doni with minimal extension efforts. The users can easily integrate them into any workload container or SDR host container, through the testbed software. For example, researchers can seamlessly transition from a high-quality 5G link between locations sdr-01 and sdr-06 to a poor-quality channel between sdr-09 and sdr-07, enabling them to empirically assess the impact on application performance. Alternatively, they can switch to a WiFi link between sdr-01 and sdr-06 for further experimentation. D. Compute Nodes ExPECA offers compute resources by allowing users run containerized applications with the most recent version of Openstack Zun that uses K8S (Kubernetes) as the orchestration layer. K8S is designed to handle a large number of containers across a variety of environments, making it well-suited for complex, large-scale deployments. Also, it offers a rich set of APIs and a pluggable architecture, allowing for greater customization and extensibility. This was particularly useful for our team when developing new desired features in Zun. These new features include 1) network attachment, 2) block storage attachment, 3) resource management i.e. specifying the number of cores and memory size for the container. Users are able to run their containerized applications on ExPECA’s Intel x86 servers, enrolled in the K8S cluster, which we refer to as ’workers’, not only to deploy and orchestrate application workloads, but also to deploy SDR communication stacks or apply radio node configurations. With the advent of Open Radio Access Network (O-RAN) and SDRs, all components of the wireless network stack can run on general-purpose processors. They can thus be containerized and distributed across multiple hosts as long as the containers are connected to the SDRs. We provide reference container images for SDR 5G, SDR LTE, and WiFi provisioning. Lastly, it is important to note that all worker nodes achieve sub-µs synchronization by exchanging PTP messages with a Grandmaster (GM) clock that itself uses GPS as a reference. Tight time synchronization is essential for users who require the same timing reference to timestamp packets across various locations within the testbed.

ExPECA: An Experimental Platform: Testbed Design and Architecture

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