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USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 31 of 33. HackerNoon’s Legal PDF Series here II. The Court Should Order Forfeiture The Court should impose the proposed forfeiture money judgment. This number is derived as follows: First, the Court should direct the forfeiture of $8,000,000,000, which is a conservative estimate of the proceeds of the defendant’s wire fraud and conspiracy to commit wire fraud on FTX’s customers, and the property involved in his conspiracy to launder the proceeds of that wire fraud. As funds that the defendant obtained through false statements and promises, and then misappropriated, they are plainly “proceeds” pursuant to 18 U.S.C. § 981(a)(1)(C). See Bonventre, 646 F. App’x at 90 (calculating forfeiture based on “an amount representing the total client investment”); United States v. Kenner, 443 F. Supp. 3d 354, 378 (E.D.N.Y. 2020) (defining proceeds forfeiture as amount “received . . . as a result of the fraud.”). The total of $8 billion also represents property involved in the defendant’s money laundering. The reason FTX had such a deficit was that the defendant and his co-conspirators had agreed to launder those funds to and through Alameda. Second, the Court should direct the forfeiture of $1,300,000,000, which represents the proceeds of the defendant’s wire fraud and conspiracy to commit wire fraud on Alameda’s lenders. This sum represents the amount that Alameda owed lenders to whom it had defrauded by making false statements about its balance sheet, calculated as of the date of the bankruptcy filing. (GX1014). Third, the Court should direct the forfeiture of $1,720,000,000, which represents the proceeds of the defendant’s securities fraud. This sum is the amount FTX raised from investors in exchange for equity on fraudulent pretenses. (GX-26 (listing the total amounts invested in the Series B, B-1, and C round investments in FTX.com)). The defendant, however, asserts that he owes no forfeiture because “customers and creditors will receive a return of their funds via the bankruptcy.” (Def. Mem. at 88). This argument is factually unsupported and legally wrong. Starting with the facts, as discussed above, it is far from certain that FTX customers and lenders will be made whole, and the bankruptcy will almost certainly not cover losses by FTX’s equity investors (the victims of the conspiracy to commit security fraud), which were approximately $1.72 billion. As a matter of law, there is no basis for reducing the forfeiture judgment based on repayment to victims in bankruptcy. For money laundering based forfeiture under 18 U.S.C. § 982(a)(1), there is no suggestion in the forfeiture statutes that repayment to victims could reduce the judgment. Indeed, Section 982(a)(1) contemplates the opposite, since it is based on the “property involved” in the money laundering. See United States v. Elfgeeh, 515 F.3d 100, 139 (2d Cir. 2008) (for money transmitting offense, which similarly applies Section 982(a)(1) for forfeiture, affirming judgment for total amount transmitted even though there are no victims); United States v. Waked Hatum, 969 F.3d 1156, 1164 (11th Cir. 2020) (in money laundering case where victims suffered no loss, affirming forfeiture judgment of total sum of laundered money, because “the government’s interest in the [laundered property] vests ‘the moment’ such property is laundered”). The same is true for wire fraud based forfeiture. While the defendant asserts that the proper measure of forfeiture here is the net proceeds under Section 981(a)(2)(B), rather than gross proceeds under Section 981(a)(2)(A), (Def. Mem. at 87-88), he is wrong. As the trial record showed, the defendant embezzled billions of dollars of FTX customer funds, and induced investors to part with money based on false statements. That is “inherently unlawful” conduct and therefore the gross proceeds rule under Section 981(a)(2)(A) applies. See United States v. Bodouva, 853 F.3d 76, 80 (2d Cir. 2017) (Section 981(a)(2)(A) applies to embezzlement); Milton, 2024 WL 779210, at *5 (selling securities for which prices have been inflated by “misleading statements” is “more analogous to cases involving Ponzi schemes” and thus Section 981(a)(2)(A) applies). And under Section 981(a)(2)(A), the law is clear: “there is no statutory authorization for an offset” even for payments to victims. Bodouva, 853 F.3d at 80. The single case cited by the defendant for the proposition that return of funds through bankruptcy should offset victim losses is a decade-old, distinguishable decision from the Northern District of Illinois. See United States v. Hollnagel, No. 10 Cr. 195, 2013 WL 5348317 (N.D. Ill. Sept. 24, 2013). There, the district court reduced a forfeiture judgment to zero because the “[d]efendants returned the investors’ investments over time” before their arrests. Id. at *4. Not only did the defendant not return the $8 billion he stole before his arrest, but the Second Circuit has explicitly rejected the reasoning of Hollnagel, under Section 981(a)(2)(B). United States v. Shkreli, 779 F. App’x 38 (2d Cir. 2019) (summary order). In Shkreli, the Court held that because “‘forfeiture is gain based’, not based on the losses (or gains) to victims,” a defendant should be required to forfeit “the total amount invested by investors” even if investors were paid back by the defendant during the scheme. Id. at 42 (citing Torres, 703 F.3d at 203). The defendant next asserts that “none of the accounts identified for forfeiture were for [his] personal benefit,” “were not profits ‘enjoyed’ by [him], but rather maintained on behalf of the companies,” and therefore are not subject to forfeiture. (Def. Mem. at 88). This is legally and factually wrong. While the word “enjoyed” is found in Hollnagel, the law is clear that the Government can forfeit corporate assets based on a defendant’s crimes when those assets were maintained through and on behalf of a company the defendant “extensively controls” or “dominates,” such that “money paid to the corporation was effectively” under the defendant’s control. United States v. Peters, 732 F.3d 93, 103-04 (2d Cir. 2013) (forfeiture proceeding under Section 982); United States v. Tartaglione, 15 Cr. 491, 2018 WL 1740532, at *21 (E.D. Pa. Apr. 11, 2018) (same for Section 981), aff’d, 815 F. App’x 648 (3d Cir. 2020). The trial evidence of course established the defendant’s control and dominance of FTX, Alameda, and its affiliated entities. Finally, the defendant asserts that the Court should order no forfeiture because he “no longer has custody of the accounts identified for forfeiture in the PSR” since the Government seized the accounts. (Def. Mem. at 88). This is utterly meritless—there is no authority even suggesting that the Government’s seizure of assets traceable or involved in a defendant’s crimes somehow precludes the Government from seeking forfeiture of the defendant’s interest in those assets. Indeed, the forfeiture laws expressly authorize seizure as part of the forfeiture process. In order for the Government to effect forfeiture of the specific property described in the preliminary order of forfeiture, the Court must first order forfeiture of the defendant’s interests in that specific property. United States v. Daugerdas, 892 F.3d 545, 549 (2d Cir. 2018) (“At stage one of [forfeiture process], before entering a preliminary order of forfeiture, the court is directed to adjudicate the government’s interest vis-à-vis the defendant”). Only once the Court has forfeited the defendant’s rights in the property can the Court move on to “stage two,” by “resolv[ing] any third-party petitioner’s interests vis-à-vis the defendant.” Id. If no third-party has a cognizable interest in the property superior to the defendant’s, then the specific property can be finally forfeited to the Government. In the preliminary order of forfeiture, the Government lists specific property beyond that listed in the forfeiture allegations in the S6 Indictment and the bill of particulars. (Dkt. 314).[14] That specific property is all forfeitable as proceeds of the defendant’s wire frauds or as property involved in money laundering. The property listed in Ex. A(1) is another account in the name of Emergent Fidelity Technologies, which was seized at the same time and pursuant to the same legal authority as the property listed in the S6 Indictment ¶¶ 38(a), (b), 40(a), (b). Ex. A(2)-(255) are the specific forms of cryptocurrency maintained in the Binance accounts described in S6 Indictment ¶¶ 38(h), (i), (j), 40(h), (i), (j). Ex. A(258) is a bank account in the name of the defendant and another FTX employee, which received transfers from the account at Silvergate Bank in the name of Alameda ending in -4456, which as described in Government Exhibit 1050 accepted customer fiat deposits. The Government also seeks to forfeit the defendant’s interests in certain contributions traceable to his criminal conduct that he, Salame, and Singh made to political candidates and committees, during the relevant period. Beginning in March 2023, the Government, working with the United States Marshals Service and the FBI, sent letters to those candidates and committees who the Government had identified had received donations from the defendant, Salame, and Singh, requesting return of the donations as forfeitable property, and because the donations were illegal. As of today, 251 candidates and committees have returned $3,348,957.23. [15] The Preliminary Order of Forfeiture lists the identified campaigns, including those that have returned funds to the Government, in Ex. A(259) to (261). As evidenced at trial, the defendant’s interests in the political donations are forfeitable because the donations were made using the proceeds of the defendant’s wire fraud on customers and were themselves manifestation of his money laundering. (GX-1039; 1044; 1088; 1089; 1090). Continue Reading . Here [14] The airplanes listed at Ex. A(256) and (257) are the property listed in the bill of particulars. [15] The Government is also coordinating with the FTX Debtors, so that the estate retrieves some of the political contributions. In some circumstances, a political candidate or committee has sought to or did return funds to the estate rather than the Government. As of today, the Government understands that the estate has received $281,356 from 8 candidates and committees. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, 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. storage.courtlistener

Court Imposes $10 Billion Forfeiture Judgement on Sam Bankman-Fried

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 1 of 7. HackerNoon’s Legal PDF Series here This matter comes before the Court on Toptal, LLC's (“Toptal”) Motion for Summary Judgment (“Motion”). In the Motion, Toptal argues that Denis Grosz (“Mr. Grosz”) cannot prove that Toptal (a) breached the Note Purchase Agreement (the “NPA”), convertible promissory note (the “Note”), or (b) the Advisor Agreement entered into by the parties on November 14, 2012[1]; that Mr. Grosz's declaratory judgment counterclaims are superfluous; and that Mr. Grosz's counterclaims are barred by the statutes of limitations. The Court has considered the parties’ motion papers, the'arguments by the parties on September 29, 2023, and October 9, 2023, and the supplemental briefing submitted by the parties on October 4, 2023. The Court finds that there are no material disputed questions of fact with respect to part (a) of Toptal's Motion - whether Toptal breached the Note Purchase Agreement (the “NPA”) and convertible promissory note (the “NOTE”) and hereby grants that part of Toptal's Motion. Given this determination, it is not statute of limitations. California Code Section 360 cannot extend the 15 day period to elect the conversion even under Grosz's reading of the statute to extend the statute of limitations period for contracts. [2] As to Mr. Grosz's counterclaim for breach of the Advisor Agreement, Toptal argues that Mr. Grosz cannot show breach of contract because Mr. Grosz has not submitted evidence of damages. Grosz rests his damage model on the testimony of expert Gregory Nachtwey. The Court damages. Grosz rests his damage model on the testimony of expert Gregory Nachtwey. The Court will defer ruling on this counterclaim until after the hearing on the Motion in Limine concerning the admissibility of Mr. Nachtwey's opinions. FACTS AND PROCEDURAL HISTORY* In 2010, Mr. Du Val founded Toptal, a single-member LLC. Mr. Du Val is and always has been the CEO and sole member of Toptal. Mr. Grosz provided Toptal $1 million pursuant to the NPA on November 14, 2012, and, in exchange, Toptal issued him the Note. As part of that transaction, the parties also entered into an Advisor Agreement on the same day, pursuant to which Mr. Grosz agreed to advise the company in exchange for the opportunity to purchase 1.5% equity in Toptal subject to certain conditions that have not occurred to this day. Mr. Grosz asserts that Toptal breached the NPA and Note by (i) failing to convert to a C-corporation and give him equity upon his written election on November 24, 2015, and/or (ii) attempting to repay his principals, and accrued interest on the Note in March 2020. Several provisions of the NPA and Note are relevant to the Motion. First, the Note states that “[u]nless earlier converted into Conversion Shares pursuant to Section 2.2 of that certain Note Purchase Agreement dated November    , 2012 among the Company and the Lender (the ‘Purchase Agreement’), the principal and accrued interest shall be due and payable by the Company on demand by the Lender at any time after the Maturity Date.”   The Note contains a provision expressly waiving presentment and demand. The NPA defines the “Maturity Date” as “May 14, 2014.” Second, Section 2.2 of the NPA is titled, .”' Section 2.2(a) provides that “[t]he principal and unpaid accrued interest of the Note will be automatically converted into Conversion Shares upon the closing of the Next Equity Financing....” A Next Equity Financing Conversion Shares upon the closing of the Next Equity Financing....” A Next Equity Financing (a defined term in the NPA) did not take place prior to the Maturity Date and has not taken place to this day. Section 2.2(b) of the NPA states: “ At any time on or after the fifteenth (15th) day prior to the Maturity Date, if the Next Equity Financing has not occurred by that time, the Note, at Lender's election by written notice to the Company, will be converted into Common Stock of the Company such that the Lender receives shares constituting 8.333% of the Fully-Diluted Equity Securities of the Company following such conversion.” “ Rieht to Convert Note Optional Conversion. The parties dispute whether Section 2.2(b) provides Mr. Grosz with a 15-day window  before the Maturity Date in which to make an election to convert his Note to equity. For its part, Toptal argues that Mr. Grosz's ability to make an election under Section 2.2(b) ended on the Maturity Date. On the other hand, Mr. Grosz argues that Section 2.2(b) allows him to elect to convert his Note starting 15 days prior to the Maturity Date and continuing forever into the future. The parties agree that Mr. Grosz did not make an election under Section 2.2(b) prior to the Maturity Date. The parties also dispute the meaning of Section 2.2(c) of the NPA, which states: “ For clarification, the Company agrees to complete a Company Conversion prior to any conversion of the Note pursuant to this Section 2.2.” Mr. Grosz claims that this provision, read in connection with Section 2.2(b), grants him the power to force Toptal to complete a Company Conversion. In other words, Mr. Grosz claims that once he makes an election under Section 2.2(b), Toptal must convert from an LLC to a C-corporation under Section 2.2(c) so that it can issue Mr. Grosz equity. Toptal disagrees that Section 2.2(c) grants Mr. Grosz the power to force Toptal to change its corporate form. Companv Conversion Prior to Note Conversion. The NPA also contains a section titled ” which provides that “[t]he following events shall be considered Events of Default with respect to the Note following, and only upon, the declaration of default by the Note holder under Section 7.2: “ Events of Default, (a) The Company shall default in the payment of any part of the principal or unpaid accrued interest on the Note for more than thirty (30) days after the Maturity Date or at a date fixed by acceleration or otherwise;. (b) The Company shall fail to observe or perform any other obligation to be observed or performed by it under this Agreement or the Note, within 30 days after written notice from the Note holder to perform or observe the obligation.” Section 7.2 of the NPA states: “Remedies. Upon the occurrence of an Event of Default under Section 7.1 hereof, only at the option and upon the declaration of the holder of the Note, the entire unpaid principal and accrued and unpaid interest on the Note shall, without presentment, demand, protest, or notice of any kind, all of which are hereby expressly waived, be forthwith due and payable, and such holder may, immediately and without expiration of any period of grace, enforce payment of all amounts due and owing under the Note and exercise any and all other remedies granted to it at law, in equity or otherwise.” It is undisputed that Toptal did not convert to a C-corporation before the May 14, 2014, Maturity Date or pay any outstanding principal and interest to Mr. Grosz within 30 days after the Maturity Date. Continue Reading . Here [I] There are no fully executed copies of these agreements in the Record. The Court has reviewed a copy of the NPA and the Advisor Agreement containing Taso Du Val's (“Mr. Du Val”) signature on behalf of Toptal, but those agreements are missing Mr. Grosz's signature. The Court has not received an executed copy of the Note. However, the Court does not address whether the lack of signatures by all parties renders the Agreements unenforceable, because Count I of Mr. Grosz's counterclaims fails as a matter of law in any event. [2] Toptal's other arguments that Mr. Grosz's remaining counterclaims are untimely is addressed in a separate order. [3] The facts set forth in this Order are the facts “viewed in the light most favorable to the non-movant,” Mr. Grosz. 121 Nev. 724, 729 (2005) (“[W]hen reviewing a motion for summary judgment, the evidence, and any reasonable inferences drawn from it, must be viewed in a light most favorable to the nonmoving party.”); 118 Nev. 288, 293 (2002); 26 Carrington Mortg. Servs., 139 Nev. Adv. Op. 13 (2023). Wood v. Safeway, Inc., Chavez v. Sievers, see also Wishengrad v. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 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. media.licdn.com

Toptal, LLC v. Denis Grosz: Summary Judgement and Court Filing Analysis

ERIK ADOLPH vs. Uber Court Filing, retrieved on July 17, 2023, is part of . You can jump to any part in this filing . This is part 15 of 15. HackerNoon’s Legal PDF Series here CONCLUSION We reverse the judgment of the Court of Appeal and remand the case for further proceedings consistent with this opinion. We limited our review to the question of PAGA standing and express no view on the parties’ arguments regarding the proper interpretation of the arbitration agreement. LIU, J. We Concur: GUERRERO, C. J. CORRIGAN, J. KRUGER, J. GROBAN, J. JENKINS, J. EVANS, J About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case S274671 retrieved on September 22, 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. courts.ca.gov

The Judgement of the Court of Appeals Is Reversed in the Uber Lawsuit

Judgement Day.

On-Line Data-Acquisition Systems in Nuclear Physics, 1969, by H. W. Fulbright et al.  National Research Council is part of the HackerNoon Books Series.
 You can jump to any chapter in this book here . Chapter 4: EXERCISING ECONOMIC JUDGMENT IN PLANNING C. EXERCISING ECONOMIC JUDGEMENT IN PLANNING Since the ultimate criterion is research output, the role assigned to a computer system must depend on the nature of the work being planned. In some cases where a very specific use is intended, for example, in the case of a process-control application such as the Argonne plate scanner or an accelerator controller, the conditions are simple enough to make economic judgment relatively easy to apply. In the case encountered in setting up an accelerator laboratory where a wide variety of experiments is to be performed, conditions are much more complex. It is now widely accepted that any such laboratory should have a computer system, but what is not so clear is how extensive and expensive it should be. In other words, points 1-4 in B are accepted, and point 5 is conceded possibly to be applicable. If sufficient funds are available, one sensible way to proceed is to use the accumulated collective experience outlined above. For example, one can say that experience has shown that the total investment in the computer system will be in reasonable balance with the capital investment in the bare accelerator if the ratio of costs is about one to five. Departures from the rule may then be made to adjust to special circumstances. Following this procedure means extrapolating from past experience, which may not prove a good guide, but this approach is similar to that often used in other matters bearing on the support of research. Probability is involved. It should be noted that the actual expenditures for on-line equipment for nuclear research have far exceeded those projected at the "Grossinger Conference on the Utilization of Multiparameter Analyzers in Nuclear Physics" in 1962. In times of economic stringency it may be necessary to take a hard look at points 3-5 in B above before deciding how large a computer can be justified. A medium-sized computer is sufficient for most data-acquisition demands but not for large-scale calculations of a theoretical nature or for an occasional complicated piece of data reduction. Often it will be advisable to plan on carrying out all large calculations at the computing center, in which case a medium-sized computer will probably suffice for data acquisition, and a saving of about half in capital investment and operating expenses can be achieved. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. W., Fulbright et al. 2013. On-Line Data-Acquisition Systems in Nuclear Physics, 1969. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/42613/42613-h/42613-h.htm#Page_70 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Exercising Economic Judgement in Planning

An Essay Concerning Humane Understanding, Volume II, by John Locke is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here: [ LINK TO TABLE OF LINK ]. Volume II: Book IV, Chapter XIV: OF JUDGMENT. CHAPTER XIV. OF JUDGMENT. 1. Our Knowledge being short, we want something else. The understanding faculties being given to man, not barely for speculation, but also for the conduct of his life, man would be at a great loss if he had nothing to direct him but what has the certainty of true knowledge. For that being very short and scanty, as we have seen, he would be often utterly in the dark, and in most of the actions of his life, perfectly at a stand, had he nothing to guide him in the absence of clear and certain knowledge. He that will not eat till he has demonstration that it will nourish him; he that will not stir till he infallibly knows the business he goes about will succeed, will have little else to do but to sit still and perish. 2. What Use to be made of this twilight State. Therefore, as God has set some things in broad daylight; as he has given us some certain knowledge, though limited to a few things in comparison, probably as a taste of what intellectual creatures are capable of to excite in us a desire and endeavour after a better state: so, in the greatest part of our concernments, he has afforded us only the twilight, as I may so say, of probability; suitable, I presume, to that state of mediocrity and probationership he has been pleased to place us in here; wherein, to check our over-confidence and presumption, we might, by every day's experience, be made sensible of our short-sightedness and liableness to error; the sense whereof might be a constant admonition to us, to spend the days of this our pilgrimage with industry and care, in the search and following of that way which might lead us to a state of greater perfection. It being highly rational to think, even were revelation silent in the case, that, as men employ those talents God has given them here, they shall accordingly receive their rewards at the close of the day, when their sun shall set, and night shall put an end to their labours. 3. Judgement or assent to Probability, supplies our want of Knowledge. The faculty which God has given man to supply the want of clear and certain knowledge, in cases where that cannot be had, is JUDGEMENT: whereby the mind takes its ideas to agree or disagree; or, which is the same, any proposition to be true or false, without perceiving a demonstrative evidence in the proofs. The mind sometimes exercises this judgment out of necessity, where demonstrative proofs and certain knowledge are not to be had; and sometimes out of laziness, unskilfulness, or haste, even where demonstrative and certain proofs are to be had. Men often stay not warily to examine the agreement or disagreement of two ideas, which they are desirous or concerned to know; but, either incapable of such attention as is requisite in a long train of gradations, or impatient of delay, lightly cast their eyes on, or wholly pass by the proofs; and so, without making out the demonstration, determine of the agreement or disagreement of two ideas, as it were by a view of them as they are at a distance, and take it to be the one or the other, as seems most likely to them upon such a loose survey. This faculty of the mind, when it is exercised immediately about things, is called JUDGEMENT; when about truths delivered in words, is most commonly called ASSENT or DISSENT: which being the most usual way, wherein the mind has occasion to employ this faculty, I shall, under these terms, treat of it, as feast liable in our language to equivocation. 4. Judgement is the presuming Things to be so, without perceiving it. Thus the mind has two faculties conversant (about truth and falsehood):— First, KNOWLEDGE, whereby it certainly PERCEIVES, and is undoubtedly satisfied of the agreement or disagreement of any ideas. Secondly, JUDGEMENT, which is the putting ideas together, or separating them from one another in the mind, when their certain agreement or disagreement is not perceived, but PRESUMED to be so; which is, as the word imports, taken to be so before it certainly appears. And if it so unites or separates them as in reality things are, it is right judgement. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Locke, John. 2004. An Essay Concerning Humane Understanding, Volume II. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/cache/epub/10616/pg10616-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Understanding Judgement 

We are banking online, shopping online, using social networking sites, playing online games, storing photos, audio, and video online and using online file-sharing. And mostly all those services are cloud-based. For example, such famous platforms as Airbnb, PayPal, Zynga, Spotify and Adobe Systems are “cloudy”. – Daddy, what are clouds made of? – Linux servers, mostly. (ba dum tss!) Cloud computing is one of the most promising and discussed fields in the kingdom of information technologies. This a common term which most have heard about but not everyone knows what exactly it means and what is going on under the hood of сloud machinery. We are banking online, shopping online, using social networking sites, playing online games, storing photos, audio, and video online and using online file-sharing. And mostly all those services are cloud-based. For example, such famous platforms as Airbnb, PayPal, Zynga, Spotify and Adobe Systems are “cloudy”. It means that cloud computing technologies have long been a part of our daily usage. The only question is how well do we understand what does it mean? CLOUD IS NOT A TECHNOLOGY, IT'S A SOLUTION Let's mark, that cloud computing is defined as a MODEL, PARADIGM, sometimes SOLUTION, but not exactly a technology, platform or product. “Cloud computing architecture is something you do, not something you buy“. David Linthicum, author on InfoWorld — If we make an extract from different definitions of cloud computing, we will get something like this: put in simple words, cloud computing is about storing, accessing, and managing a wide range of data and programs over the Internet instead of your hardware. It is rooted in the good times of old school presentations showing the giant Internet infrastructure in the shape of a cloud that accepts connections. And if we need more deep and scientific meaning let’s take this one: NAME IS DECEPTIVE, OR SERVERLESS IS NOT A SERVERLESS… As far back as 1994, scientists used a cloud-shaped drawing to portray clusters of services that could be delivered over the Internet. But still, the term "cloud" is staying blurred for many people. Hope we have already figured out a "cloud computing" term, let's move on. One much more confusing term is "serverless computing" as one of a cloud IT model, that actually doesn't eliminate servers from distributed applications. “The term "serverless" is annoying as heck. Sure, you're not fiddling with nginx configs, but you're even more tightly dependent on running in a cloud environment. It should be "servermore". — skewart on Hacker News according to Amazon. In the classic cloud model, you are paying for a constantly running server or container. Serverless apps have functions running in the response of such events as file uploads or web requests. The most famous computing platform for such a deployment model is AWS Lambda. "Serverless applications don't require you to provision, scale, and manage any servers," It's kinda equivalent to a timeshare condo vs renting a condo. Your functions only run when they are called. — quaz 31 on Hacker News CLOUD COMPUTING IS NOT REMOTE STORAGE ONLY Concepts and are muddled up often. This couple is interconnected but not the same. Remote cloud storage systems, such as Google Drive, Dropbox, or iCloud, may enable you to host, share, sync and edit files only from multiple devices using the Internet. "cloud storage" "cloud computing" Such a system allows clients to send files to data servers on the Internet, instead of storing it on a computer or external storage device. And a cloud computing machine is more than permanent store or data sharing tool. It's abilities are much wider. It's about the use of hardware and software to deliver varied services over a network while cloud storage is simply storing data and files, and performing backups for completing the process. THE POWER OF THREE: IAAS, PAAS, SAAS… AND SOMETHING MORE Cloud computing building consists of 3 basic levels: the lowest is . It is a basis of 'bare metal' providing the physical servers, device storage, networking components. is the middle that maintains an environment for hosting user apps, such as virtual servers, operating and database environments. It's demanded to develop, run, and manage apps without the need for infrastructure organization. PaaS is a simple way to deploy your app in a given technology (e.g., Node.js, .NET, Python, Java, Ruby, etc). The top is providing tan out-of-the-box software including service on demand. It's a software distribution model where a third-party vendor hosts apps and makes them available to clients over the Internet. In SaaS a lot of areas can be managed by providers: apps, runtime, data, OSes, virtualization, servers, storage, etc. One more room on this top floor is as a subset of SaaS. More specifically, it's an API (usually HTTP) returning some data e.g., currency exchange rates or weather forecasts, DaaS examples: Google Maps, Google Translate API, AccuWeather. But that is not a full-house yet. Let's look on upstairs. There we can single out a Function-as-a-Service (FaaS), an event-based architecture: functions can be triggered by a given event. It implies a very high level of abstraction that it's called a serverless architecture (see above: name is deceptive ;) ). To quickly relate let's consider Google's tools: Infrastructure-as-a-service (Iaas) Platform-as-a-Service (PaaS) Software-as-a-Service (SaaS) Data-as-a-Service (DaaS) HYBRID OR NOT HYBRID: THAT IS THE QUESTION It's common to identify three ways of deploying cloud resources: private, public and hybrid. While the first duo is clear for understanding, the third one is still confusing. Let's try to handle this. Cloud computing becomes into a most real omni-cloud journey. The optimal cloud solution for most businesses is neither public or private, but both. Traditionally, it is called a hybrid cloud, that mixes different ways of deployment. To be honestly, a hybrid approach has spawned a lot of misunderstandings. In fact, such a model doesn't match the common term "hybrid", that is traditionally defined as something that has been bred from two different species as an individual object and primarily involves crossbreeding as process. But in fact the hybrid cloud deployment is a specific strategy where data may intermingle between private and the public clouds, but it remains with the same workload. So the hybrid cloud is about distributing app components in various clouds and sharing data between them while the heart of the process, i.e. actual executables are functioning in only one cloud environment. “Much of the adaptation to hybrid/multi-cloud focuses on how to move data back and forth, how to run applications on disparate hypervisors and how to bring non-standard services together. — Chris Evans, author on Architecting.it Now we are getting close to multi-cloud concept. In accordance with Gartner, multicloud strategies will reduce vendor dependency for two-thirds of organizations through 2024. This strategy implies mixing multiple cloud services, whereas they are not connected or orchestrated between themselves. Such multi-vendor model gives a great opportunity to obtain the most efficacious solutions for business, while hybrid cloud refers specifically to the specific tech portfolio. Let's mark that multi-cloud strategy may include hybrid cloud as a part, but a hybrid cloud solution isn't always a multi-cloud one. THERE IS NO BEST OF THE BEST CLOUD SERVICE Cloud tech is gaining huge attention for today. Such corporate giants as Microsoft, IBM, Google, Amazon provide clouds and successfully use it. These systems are all kind of different but they have the same character, i.e. the same concept. Depending on the features we are looking for, we can try several services and make use of benefits of cloud computing provided by different companies. Enterprise can use different cloud environments, implementing one workload in Google Cloud or Microsoft Azure, another in Amazon Web Services, and maybe even more, for example, you can keep some data on a private cloud. The advent of cloud technologies revealed a lot of new business scenarios. Marketing solutions like Adobe Marketing Cloud or Hubspot, tools for education like SlideRocket (ClearSlide), ClearDATA in the healthcare sector: different areas have their own advantages of cloud computing innovations and new increasing opportunities for implementing in this deployment model. When it comes to dilemma 'cloud vs on-site', start from your organization's specific needs and requirements. Many companies combine both models, mixing public cloud services and on-site for various apps looking for the perfect combination of scalability, cost savings and efficiency managing. This allows them to get the best of both worlds, using the advantages of cloud computing in full. ( r: ) Disclaime The author works at FreshCode (Originally published ) here

Cloud Computing is Replete with Clouded Judgements #BreakTheChain

Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable.
If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information.
If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information.
We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q.
Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0.
Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model.
PSI values close to zero indicate stability between the initial and new populations.
Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article. Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X ), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. X Entropy and KL Divergence Entropy and KL Divergence Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Population Stability Index Population Stability Index Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article.

Population Stability Index in Machine learning models

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 2 Background and Related Work The European Parliament (EP) serves as an important forum for political debates and decisionmaking at the European Union level. Members of the European Parliament (MEP) are elected in direct elections across the EU. The European Parliament debate is presided over by the President, who guides MEPs in discussing specific subjects. EP debates have been the source of three key datasets. First, was crafted by Koehn (2005) by collecting EP debates documents from 1996 to 2011, and extracting translations as a parallel corpus for statistical machine translation, enriched with attributes including , and Europarl-2005 debate date chapter id, MEP id, language, MEP name, MEP party. Later, Rabinovich et al. (2017) built upon , by introducing additional demographic attributes: MEP gender and . These were sourced from sources such as Wikidata (Vrandeciˇ c and Krötzsch ´ , 2014) and automatic annotation tools such as *Genderize[*2] and . [3] However, is limited to only two language pairs: English– German and English–French. (Vanmassenhove and Hardmeier, 2018) expanded upon to add twenty additional language pairs, based on the manual translations in the EP archives. These corpora have been used primarily for machine translation research. Europarl2017 Europarl-2005 MEP age AlchemyVision Europarl-2017 Europarl-2018 Europarl-2017 Since 2020, the EU has publicly released raw debates in the form of transcribed source-language speeches with rich multilingual topic index data, along with the original video and audio recordings. This forms the basis of the Multi-EuP dataset, with additional attributes for each speaking MEP such as an image, birthplace, and nationality. Zhang et al. (2021) introduced Mr. TYDI, an evaluation benchmark dataset for dense retrieval assessment over 11 languages. This dataset is constructed from TYDI (Clark et al., 2020), a question answering dataset. For each language, annotators assign relevance scores as judgments for questions, derived from Wikipedia articles. Notably, the questions for different languages are crafted independently, and relevance judgements are provided in-language only. Based on the dataset, the authors evaluate on monolingual retrieval tasks for non-English languages using BM25 and mDPR as zero-shot baselines. However, Mr. TYDI’s scope is limited in that it is not truly multilingual, in that queries in a given language are only performed over documents in that language. This is part of the void our work aims to address. MS MARCO (Nguyen et al., 2016) is a widelyused dataset, sourced from Bing’s search query logs, but for English queries and documents only. To mitigate this, Bonifacio et al. (2021) introduced mMARCO, a multilingual variant of the MS MARCO passage ranking dataset, spanning 13 languages and created through machine translation, based on one open-source approach (Tiedemann and Thottingal, 2020) and one commercial system in the form of Google Translate.[4] Analysis of the authors’ results reveals a positive correlation between translation quality and retrieval performance, with higher translation BLEU scores yielding improved retrieval MRR outcomes. However, similar to Mr. TYDI, mMARCO focuses on in-language retrieval only for multiple languages, rather than multilingual retrieval. Throughout the past few decades, numerous datasets and tasks pertaining to multilingual retrieval have been developed for evaluation, through efforts such as CLEF, TREC, and FIRE, each contributing standardized document collections and evaluation procedures. These evaluation datasets facilitate genuine multilingual IR research such as Rahimi et al. (2015) and Lawrie et al. (2023). However, the scope of these datasets is generally limited to a small number of queries. For example, in the case of CLEF 2001-2003, each edition encompasses a mere few dozen queries. This limitation tends to confine research predominantly to evaluation and not offer a resource for training a multilingual ranking model. Our dataset is of a scale to accommodate both large-scale training and evaluation of multilingual retrieval methods. Compared with the related work above, our work augments the multilingual mixture of queries and documents compared to Mr.TYDI, preserves the authenticity of multilingual contexts compared to mMARCO’s translation-based approach, and surpasses the query count limitations of tasks like CLEF. [2] https://genderize.io/ [3] https://www.ibm.com/smarterplanet/us/en/ ibmwatson/developercloud/alchemy-vision.html [4] https://cloud.google.com/translate

Multi-EuP: Analysis of Bias in Information Retrieval - Background and Related Work

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix Abstract We present Multi-EuP, a new multilingual benchmark dataset, comprising 22K multilingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias. We report the effectiveness of Multi-EuP for benchmarking both monolingual and multilingual IR. We also conduct a preliminary experiment on language bias caused by the choice of tokenization strategy. 1 Introduction Information retrieval (IR) classically uses a retrieval model to query a document collection and return a ranked list of documents which are predicted to be (decreasingly) relevant to the query. Retrieval models have increasingly been based on supervised learning, involving the annotation of documents with relevance scores relative to a given query, and the training of models to predict the relative association between a query and document (Karpukhin et al., 2020; Khattab and Zaharia, 2020). In parallel with these advances, the democratisation of the internet has led to a surge of individual contributors serving as information disseminators, hailing from various countries and regions, and posting in different languages. This has created possibilities for exploration of cross-lingual and multilingual text retrieval. Cross-lingual retrieval pertains to scenarios where queries are formulated in one language but documents are retrieved from another language. On the other hand, multilingual retrieval involves a query in one language but retrieval of documents across multiple languages simultaneously. An important consideration in any such work is both robustness and fairness across different combinations of languages – for instance, are results from one language consistently ranked higher than another for certain types of query. While progress towards multilingual retrieval through the release of datasets such as Mr. TYDI (Zhang et al., 2021) and mMARCO (Bonifacio et al., 2021), both are limited in that they evaluate monolingual retrieval for a range of languages, rather than true multilingual retrieval, using multiple languages simultaneously. Additionally, mMARCO was created by machine translation of MS MARCO (Nguyen et al., 2016), introducing a confounding factor of translation errors. We present a multilingual dataset based on the European Parliament debate archive with queries in 24 distinct languages, and relevance judgements also across all 24 languages. This ensures the “multilingual” nature of the dataset in terms of both query-to-document and document-to-query associations. We additionally augment each document with comprehensive metadata of the author, including gender, nationality, political affiliation, and age, for use in exploring fairness with respect to protected attributes. Our work contributes to the field in three main ways: (1) we construct and release the Multi-EuP dataset, a resource for multilingual retrieval over 24 languages, effectively capturing the multilingual nature of both queries and documents; (2) we explore language bias within the realm of multilingual retrieval, revealing that multilingual IR using BM25 indeed exhibits notable language bias; and (3) we supplement the dataset with rich author metadata to enable research on fairness and demographic bias in IR.[1] [1] The Multi-EuP dataset is available for download from https://github.com/jrnlp/Multi-EuP.

Multi-EuP: Analysis of Bias in Information Retrieval - Abstract and Intro

Developer’s Mindset In Growth Projects

বৃদ্ধি প্রকল্পে বিকাশকারীর মানসিকতা

Die Denkweise des Entwicklers in Wachstumsprojekten

Mentalidad del desarrollador en proyectos de crecimiento

L'état d'esprit du développeur dans les projets de croissance

विकास परियोजनाओं में डेवलपर की मानसिकता

成長プロジェクトにおける開発者の考え方

성장 프로젝트에 대한 개발자의 사고방식

Mindset do desenvolvedor em projetos de crescimento

Мышление разработчика в проектах роста

Büyüme Projelerinde Geliştiricinin Zihniyeti

Tư duy của nhà phát triển trong các dự án tăng trưởng

开发商在增长项目中的心态

Workplaces are filled with moments when it’s easy to lose your calm. Uncontrollable, sudden, and intense emotions that overwhelm you, can dramatically and unexpectedly lead to an emotional outburst. Negative emotions like anger, fear, or frustration show up when your expectations aren’t met or people say or do things that conflict with your personal values and aspirations. Ignoring the conflict or whatever’s bothering you for too long creates inner turmoil—it makes you react to other people or the things in your environment. You act without thinking. You become a of your thoughts and circumstances. victim Not being in control of your emotions can make you say or do things that you regret later. You may shout at a coworker. Say impolite things to a junior member of your team. Burst into tears when others disagree with you. These emotional outbursts may not only lead to extreme distress; they’re also damaging to your relationships at work. Others can’t see the inner conflict bubbling up inside—they can’t make sense of why you are calm one moment and agitated the next. No doubt they’ll find your reaction inappropriate and unnecessary. What’s done is done. You can’t go back in time and fix your overreaction, but you can certainly build emotional agility—move on with curiosity, courage, and compassion. Emotional agility is not only about keeping your emotions in check and not lashing out your frustrations at others, it’s also about what you do once you react in unintended ways. Here are the 5 practices to prevent strong emotions from overwhelming you, take control of the situation, and do damage control if you do end up acting out in an unhealthy way: Instead of Emotional Avoidance, Lean Toward Emotional Acceptance Hundreds of thoughts will run through your mind after acting in undesirable ways: Everyone will think I am crazy. No one will speak to me. I will not get more opportunities. All these thoughts can consume so much of your time and energy that you can fail to connect with your emotions. You may avoid negative emotions with the hope that they will go away. But avoidance is like a prison. It prevents you from experiencing critical life lessons. You fail to build the skills necessary to deal with discomfort. Every repeated cycle of negative emotions leading to avoidance makes it powerful and weakens your ability to deal with difficult situations. Noam Shpancer, professor of psychology at Otterbein University and a clinical psychologist specializing in the treatment of anxiety disorders says — “Attempts at avoiding negative emotions are usually futile. Telling yourself that a certain emotion is intolerable or dangerous traps you in constant vigilance regarding the very thing you're trying to avoid. You become hyper-vigilant about any possibility of this feeling arising. The fear of the impending negative experience becomes a negative experience in itself.” This also applies to how you deal with your emotions after losing your calm at work. Instead of confronting your feelings, you may believe that the best way to deal with them is to put them out of your mind. But ignoring or avoiding makes these feelings much harder to overcome while acknowledging them can result in real personal growth. Instead of stifling or pushing them away, what if you learn to accept your emotions? Accepting does not make them true. It just allows your emotions to be what they are without judging them or trying to change them. Seeing the emotion for what it is without attempting to get rid of it shifts you from emotional avoidance towards emotional acceptance. Noam Shpancer calls emotional acceptance a far better strategy than avoidance because accepting, acknowledging, and absorbing them makes you spend less energy in pushing the emotion away, and instead, pursue the behaviors that are aligned with your goals and values. He says “Emotions, when viewed as part of a spectrum of available sources of information, are a bit like the weather report. They are important to know, consider, and understand, but they are not necessarily the overriding factor in your life plans. When the weather is bad (not to your liking), it doesn't mean you have to deny it, focus all your attention on it, or cancel your plans because of it. What you need to do is accept the weather and adjust your plans accordingly.” So, the first step to dealing with an emotional outburst at work is to come in touch with your real emotions—become fully aware of them as they show up. Mindfulness guides us to become more emotionally agile by allowing us to observe the thinker having the thoughts. Simply paying attention brings the self out of the shadows. It creates the space between thought and action that we need to ensure we’re acting with volition, rather than simply out of habit. But mindfulness is more than knowing ‘I’m hearing something’, or being aware ‘I’m seeing something’, or even noticing ‘I’m having a feeling’. It’s about doing all this with balance and equanimity, openness and curiosity, and without judgement. It also allows us to create new, fluid categories. As a result, the mental state of mindfulness lets us see the world through multiple perspectives, and go forward with higher levels of self-acceptance, tolerance and self-kindness. — Susan David Mindfulness will not only help you better manage your emotions after losing your calm; it will enable you to avoid such emotional outbursts in the future—learning to be present in the moment without judgment and observing your feelings without being overwhelmed can create space for better understanding and acceptance of your emotions, making it easier to manage them effectively. Name the Emotion to Tame the Emotion After an emotional outburst, you’re bound to feel some very strong emotions. It’s not enough to acknowledge them. You also need to give words to your feelings. In other words, say to yourself, out loud, what negative emotion you’re experiencing, as you’re experiencing it. For example: I am feeling embarrassed. I am feeling ashamed. I am feeling angry at myself. Simply naming it is going to calm you down. This technique introduced by psychologist Dan Siegel creates a bit of space between you and that emotion. Naming your emotions tends to diffuse their charge and lessen the burden they create. There’s another advantage of naming emotions this way. Matthew Lieberman is a Professor of Psychology at the University of California Los Angeles (UCLA). His research has shown that labeling of negative emotions, also called can help people recover control. His fMRI brain scan research shows that labeling of emotions decreases activity in the brain’s emotional centers, including the amygdala. “affect labeling” And once your amygdala is calm—that part of your brain involved in mode—it gives you the chance to take a step back and come up with a more thoughtful response. “fight-flight-freeze” In the brain, naming an emotion can help calm it. Here is where finding words to label an internal experience becomes really helpful. We can call this “Name it to tame it.” And sometimes these low-road states can go beyond being unpleasant and confusing—they can even make life feel terrifying. If that is going on, talk about it. Your inner sea and your interpersonal relationships will all benefit from naming what is going on and bringing more integration into your life. — Daniel J. Siegel Instead of letting your unconscious brain exaggerate the negative effects of a temporary emotional setback making you feel powerless and helpless, recognizing and naming the emotion can give you a sense of control enabling you to choose a more appropriate response. Don’t Settle for Superficial Reasoning, Dig Deeper Connecting with the emotions and naming them gives you the mental clarity needed to think clearly. When your thinking is not clouded by emotions, you can face the facts of what really happened—what caused you to behave this way. By understanding what caused you to lose your calm, you can do better damage control and can also prevent it from happening again. Engaging in deep self-reflection can help you uncover the real reason behind your outburst as opposed to what initially appeared to have triggered it: What situation were you in when you had an emotional outburst? What about the situation you did not like—was it something someone said or did, was it the way you were treated, were you denied something that was important to you, were your beliefs and values challenged in some way? Was it the first time you felt this way or has it happened before? What did you say to yourself at that moment? What caused you to finally lose your calm—dig deeper into the real reason and not what appears to be the issue at first. For example: You may snap at a coworker after your manager tells you that you have been passed up for a promotion. You may yell at an intern after a recent conflict with a coworker who blamed you for delaying the project. In these situations, what you’re experiencing is unhandled frustration which leads to psychological displacement, a term coined by Sigmund Freud which is an unconscious defense mechanism in which your mind substitutes a new aim or a new object for goals felt in their original form to be dangerous or unacceptable. It operates in the mind unconsciously with transference of emotions, ideas, and wishes in the face of aggressive impulses. By ignoring whatever annoys you about work, you let the inner conflict bubble up inside. The stress and anxiety that comes from unhandled frustrations can cause you to overreact. Amy Arnsten, an American neuroscientist explains how stress can impair judgment “The release of high levels of catecholamines during uncontrollable stress rapidly switches the brain from a thoughtful, reflective state to a more unconscious, reflexive state. This may save our lives when we are in danger, but it is often unhelpful when we need top-down control to properly guide our actions and decisions.” Knowing how the environment is pulling your strings and playing you is critical to making responsive rather than reactive moves. — Ronald Heifetz By understanding your triggers, you can prevent your autopilot mode from kicking off destructive behaviors and instead deliberately engage in constructive actions. Instead of Berating Yourself, Engage in Self-Kindness An emotional reaction that you’re not proud of can turn you into a self-critic. You may think that being harsh and mean to yourself will set you right, but engaging in self-criticism at such moments is the worst thing you can do to yourself. Self-criticism involves a harsh and judgmental internal dialogue in response to perceived failures, mistakes, or shortcomings. It often includes negative self-talk, self-blame, and self-condemnation. Having a rigid and unforgiving attitude towards yourself can make you feel shameful, guilty, and inadequate. It can lead to increased stress, anxiety, diminished sense of self-worth, and a cycle of negativity that undermines self-confidence and well-being. Instead of engaging in self-criticism after an emotional outburst, try being self-compassionate. Self-compassion involves responding to yourself with kindness, understanding, and acceptance when faced with difficulties or when you fall short of your own expectations. Self-compassionate people take a more balanced and realistic view of their imperfections which fosters a sense of worthiness and self-acceptance. They understand that everyone makes mistakes; it is a natural part of being human. The biggest misconception about self-compassion is that it can undermine your motivation to push yourself to do better; being warm and kinder to yourself will make you lazy. Quite the opposite. Self-compassion is strongly associated with emotional resilience. Self-compassion does not mean avoiding responsibility for your actions or ignoring areas in which you need to improve. Instead, self-compassion involves approaching yourself with kindness and understanding, even when you make mistakes or face difficulties thereby fostering a healthier and more positive relationship with yourself. shows that being compassionate to oneself builds emotional resilience and psychological well-being. People who are self-compassionate: Research Have lower levels of depression and anxiety. Less likely to ruminate on negative thoughts and emotions or suppress them. Show increased optimism. Are able to take personal initiative. Involved with more positive emotions like kindness, happiness, and connectedness. For example, after an emotional outburst: Instead of: I can't believe I messed up. I'm so stupid. Say: It's okay to make mistakes; everyone does. I can be kind to myself and learn from this experience. The truth is this: sometimes we display good qualities and sometimes bad. Sometimes we act in helpful, productive ways and sometimes in harmful, maladaptive ways. But we are not defined by these qualities or behaviors. We are a verb not a noun, a process rather than a fixed “thing.” Our actions change—mercurial beings that we are—according to time, circumstance, mood, setting. — Kristin Neff Don’t Just Apologize; Own Your Reaction Finally, you have to face the people who experienced your emotional outburst. You can’t ignore them or pretend as if nothing happened. You may try to put the reaction behind you and just go on with your day with the assumption that everyone will forget about it, and things will get better on their own. But, not owning how you behaved makes things awkward for others. They may feel uncomfortable around you and hesitate to approach you or avoid communicating with you altogether. Not communicating what led to an emotional outburst has another problem—when others don’t know what made you lose your calm, they’re free to create their own version and treat it as a source of truth. She’s moody. She’s completely unpredictable. She is rude and mean. To avoid being labeled after an emotional outburst, own your reaction. To do this: Don’t just apologize, talk about what led to overreaction. When it’s something that you can’t talk about openly, share a general description e.g., personal issue, conflict at work, lack of understanding, etc. Doing this will help others see that you’re just another human with flaws and imperfections. Reassure them that you’ll do your best to avoid emotional drama and have productive conversations. Reassurance goes a long way in rebuilding broken trust. If it happened due to a problem that can be discussed with them, seek out their advice on what must change to prevent it from happening to you or others again. Bringing others into the equation shows them that no one is immune to such outbursts. For example: Instead of: I am sorry that I lost my temper yesterday and yelled in front of the team. Say: I am sorry for how I reacted yesterday. I reflected on my behavior later during the day and realized that even though I was angry due to a recent conflict at work, I shouldn’t have lashed out my frustration at you. My behavior was completely unjustified and I promise not to engage in such unproductive expressions. I hope we can move on and can continue collaborating together. Strength is about how you show up. It requires you to choose what energy and action you want to bring to a given situation. At its heart, Strength is about self-management. It’s not about controlling your emotions—it’s about honoring them and choosing what you do next. — Darcy Luoma Summary Negative emotions at work can sometimes be so intense that they take away your ability to think clearly. Emotional fog can make you lose your calm and respond to others in unproductive ways. Hiding away or trying to avoid facing the reality of what happened will not fix the problem; it will only make it worse. Instead of avoiding the emotions after an emotional outburst, come in touch with your real feelings. Acknowledge them without judging them or trying to change them. Next, describe exactly how you’re feeling by naming the emotion. This practice will calm you down and give you an opportunity to take a step back and think clearly. With a clear head, you’ll be better positioned to reflect on your behavior and identify the root cause of your emotional outburst and what really triggered it. Criticizing yourself for your overreaction is a bad strategy. Instead of fixing the situation, it will keep you locked in a cycle of negative thinking. Practice self-compassion in such moments by giving yourself warmth, kindness, and empathy. Finally, own your mistake. Showing you aren’t perfect and vulnerable much like other human beings will bring you closer to others and strengthen your relationships at work. This story was previously published . Follow me on or here for more stories. here LinkedIn

How to Recover From an Emotional Outburst At Work

কর্মক্ষেত্রে একটি মানসিক বিস্ফোরণ থেকে কীভাবে পুনরুদ্ধার করবেন

So erholen Sie sich von einem Gefühlsausbruch am Arbeitsplatz

Cómo recuperarse de un arrebato emocional en el trabajo

Comment se remettre d'une explosion émotionnelle au travail

कार्यस्थल पर भावनात्मक आवेग से कैसे उबरें

職場での感情の爆発から立ち直る方法

직장에서 감정이 폭발했을 때 회복하는 방법

Como se recuperar de uma explosão emocional no trabalho

Как оправиться от эмоционального всплеска на работе

İşyerinde Duygusal Bir Patlamadan Nasıl Kurtulabilirsiniz?

Làm thế nào để phục hồi sau cơn bộc phát cảm xúc tại nơi làm việc

如何从工作中的情绪爆发中恢复过来

Newton’s Laws in Society: How Social Forces Move Physical Bodies

সমাজে নিউটনের আইন: সামাজিক শক্তিগুলি কীভাবে শারীরিক দেহগুলিকে স্থানান্তরিত করে

Newtons Gesetze in der Gesellschaft: Wie soziale Kräfte physische Körper bewegen

Las leyes de Newton en la sociedad: cómo las fuerzas sociales mueven los cuerpos físicos

Les lois de Newton dans la société : comment les forces sociales déplacent les corps physiques

समाज में न्यूटन के नियम: सामाजिक शक्तियां भौतिक निकायों को कैसे स्थानांतरित करती हैं

社会におけるニュートンの法則: 社会力がどのように物体を動かすか

뉴턴의 사회 법칙: 사회적 힘이 육체를 움직이는 방법

As Leis de Newton na Sociedade: Como as Forças Sociais Movem os Corpos Físicos

Законы Ньютона в обществе: как социальные силы движут физическими телами

Newton'un Toplumdaki Yasaları: Sosyal Güçler Fiziksel Bedenleri Nasıl Hareket Eder?

Định luật Newton trong xã hội: Các lực lượng xã hội di chuyển cơ thể vật chất như thế nào

社会中的牛顿定律：社会力量如何移动身体

Understanding the Severity of Bankman-Fried's Crimes

“ ” — Brené Brown, professor and author. No vulnerability, no creativity. No tolerance for failure, no innovation. It is that simple. If you’re not willing to fail, you can’t innovate. If you’re not willing to build a vulnerable culture, you can’t create. I believe there to be two types of people in the workplace: those who strongly believe being vulnerable at work is a sign of weakness that will be used against them, and those who have had that happen but still do it to stay authentic, hoping it will pay off in the long run. I’ve spent years working with both types. I’ve also worked in other people’s teams, as well as built my own. In this article, we will discuss why only sabotages the team, the company, and the individual and how vulnerability can have an incredible impact if used correctly. an invincible engineer mentality Me against the world In the current job market, where an average software engineer , where remote work and freelancing lead to , and companies are with little to no notice, it’s easy to enter the mentality. stays just two years at a single company engineers having 2 or 3 full-time positions at the same time firing thousands of software engineers me against the world This way of thinking leads us to always compete with everyone else for superiority, material prosperity, and recognition. Pushing it further reduces any chance for real, meaningful connections and can instill paranoia. Shutting everyone out gives us a false sense of security, as no one can really hurt us (or at least they won’t know they did.) When I first started working, I was overwhelmed with the feeling of incompetence. Surrounded by brilliant engineers who talked in terms I didn’t understand, the adaptation was hard. Every time I had to ask for help or when something didn’t work, I felt shame. To cope with it, I followed my instincts and emotionally shut out, trying not to feel overcome by agitation and shame. Two years in, this was still my main coping mechanism. Whenever frustrations with myself or my colleagues would arise, I’d try to ignore them and just move on because that way, no one could judge me for my weaknesses and shortcomings. Looking back at my 10 years of professional development, the single greatest impact on it was accepting my emotions, my limitations in knowledge and capabilities, and being open about them. A wake-up call came from someone dear to me, who was, in many ways, my mentor. They helped me realize that I was bottling up emotions - a situation that’s neither sustainable nor optimal. After that, it was up to me: would I adapt my instinct reactions and start showing real vulnerability? I believe I’ve made the right choice. To this day, it helps me stay authentic and honest, gives power to my words, and helps me connect with people more easily. Showing vulnerability in a team Great software companies don’t measure productivity and output at an individual level but at the team level. This not only incentivizes everyone to work together but also disrupts the mentality. me against the world A lot of the successful teams’ dynamics come down to trust. Trust between team members is the glue that holds everything together when things get hard. Everyone has good and bad days — sometimes that means things don’t get communicated correctly or at all. Working closely with someone means there will be a million opportunities to be disappointed or frustrated. In these times, we need to trust that someone doesn’t mean us harm, giving them the benefit of the doubt and be honest if things aren’t working out. If the trust is mutual and the goals are aligned, it will be a net-positive game. Great teams don’t have perfect members. Instead, they have honest, open communication that nurtures trust and a feeling of unity. I’ve been a part of both toxic and nurturing teams, and the dynamics are completely different. Toxicity disrupts everything, from communication to cooperation and delivery speed. Something I’ve learned over the years is that when faced with toxic team members, their attitudes and behavior need to be addressed directly, no matter their brilliance and output. Otherwise, their negative influence will pull everyone down and affect the team spirit. The first step in addressing such situations is direct and honest communication, expressing our observations and feelings. I believe people are inherently good, and toxicity comes from unaddressed issues in the past coupled with some personal insecurities. Although the past can’t be changed, together, we can commit to doing better in the future. Talking privately with a person about the negative feelings they might be causing is incredibly hard. The first time I organized such a conversation was with a senior peer, and it was one of the scariest things I ever did in my career. It was long, intense and honest. We were both open, constructive, and non-judgmental. By the end, I had massive, newly-found respect for my colleague, even though things didn’t work out in the long run. If we can find that common language with such individuals, the focus switches from “addressing toxicity” to “mending relationships,” which is a much more constructive outlook. If we then successfully continue to improve that relationship, things will start to heal, and trust will slowly be re-established! If it goes the other way, at least we gave it our best. Champions of vulnerability An environment that cherishes vulnerability also encourages authenticity and gives employees a feeling that they can speak up and make suggestions, agree and disagree, and admit mistakes. It entices learning through sharing and creating genuine connections with the team. The first step towards creating such a culture is leading by example — not compromising on honesty and accepting the hard and uncomfortable conversations. It is also important to educate others about it, about the benefits, and about the misconceptions that showing vulnerability is a sign of weakness. Vulnerability is something we cannot enforce, as it would have the opposite effect. It starts with a safe environment, people who care about one another, and who focus on learning over outcomes. Working in a startup is especially challenging — the hours are long, the pay is bad compared to many other places, the documentation is lacking, and the processes are semi-defined at best. Having interviewed hundreds of candidates, I realized that the best filtering strategy is showing real vulnerability. I am very open about all these downsides and will emphasize them in interviews. Some candidates will be attracted by this and will see these challenges as opportunities to grow and make an impact. Most will say And I’m OK with that, as it saves us all some time, regardless of the fact that we might miss some incredible engineers. thank you, no thank you. Not compromising on vulnerability is incredibly powerful and has lasting effects. I realized that during my last vacation when my team had two production outages. They realized there was something wrong, alerted the clients, did damage control, fixed the mistakes, and made sure something like this wouldn’t happen again — all while I was gone. From what I heard, it was extremely stressful, intense, and at times unpleasant. During all that time, there was no shaming or finger-pointing. Everyone tackled it as a team. Furthermore, the individuals in charge of the failing systems immediately spoke up when they detected something was wrong and chose not to hide. They showed real vulnerability and courage, and everyone else rewarded them with support. This wouldn’t have happened if our sole priority was engineering excellence and not finding a cultural fit. If we’re not in a position of high influence, we can’t really expect to change the whole company, its hiring processes, or its core principles. However, we can influence the team we work in. We can share our feelings and ideas, honestly talk about potential improvements, actively listen, and be vulnerable. We can be champions of vulnerability — role models for others to do the same. Conclusion There are two types of people in the workplace: those who feel alone and threatened by others and those who treat work as a net-positive game. By being vulnerable with our team members, we instill trust and a sense of connection. We empathize with colleagues who struggle, doubt themselves, or feel overwhelmed. We demonstrate a strength of character and self-awareness. These are the foundations that build great teams. With challenges brought by remote work and a crazy job market, it is very important to prioritize what really matters, which is people. If you’re someone in an influential position, create an environment where people will feel appreciated, included, and relied upon. And if you’re someone who’s just starting out, find a place where your potential and passion will be cherished and your failures treated as learning opportunities. We need courage and grit to be vulnerable — that’s hard. But everyone will know us for our authentic, honest selves, which pays off in the long run. Acting invincible just doesn’t pay off. P.S. To learn more about vulnerability, I suggest you read . It helped me better understand things I see in my everyday life. Daring Greatly by Brené Brown Also published . here

Death To The Invincible Engineer: Embracing Vulnerability in the Workplace

Bitcoin is a fire breathing Dragon, A Quantum Computer, an Ice King. Fire makes the economy go round, and Ice brings on necessary winters that reset the planet for the next crop. The hotter the bitcoin, the more secure The colder the qubit, the more pure Exponentially they run in unique directions. Exponentially they become ever more useful. Humanity is lucky to have Bitcoin and Quantum Technologies. I am lucky that these two are destined for marriage. Converging at the nanoscale to create me, and you, an infinity stone. Circa 2100 Circa 2100 CE, it will be the 22nd century. Nobody knows what will happen then. But I know two things, quantum computational technologies will be more mature, and Bitcoin will also be more mature. They shall no longer be teenagers; they shall be wise sages then. Powerful sages. The maturity will come in many forms. For Bitcoin, a lot more people will be orange-pilled. When the US Dollar Debt balloons to a nice $100 trillion, you can be sure illions of people will be Bitcoiners. B It is incredible. If any country on earth spent , its economy would soon be in hyperinflationary hell, to collapse not even a year later. But if that country is the USA, via the Federal Reserve, it enjoys a wide swimming pool thanks to global reserve currency status. $1 trillion dollars of debt every 100 days But that is not all. The US Dollar also enjoys a ‘ ’ in the form of investments flowing in from outside the USA or its US Dollar.  I mean, most Bitcoiners are Americans, so Bitcoin is keeping the US Dollar afloat! Dollar Milkshake Let that sink in. *** So, circa 2100, the US Dollar still rules the Iron Throne. It keeps growing big and strong from all the milkshakes it enjoys, and then there is the one provided by Artificial Intelligence, feeding on our internet data. At $100 trillion in debt, subways in New York City are now rags, woke-ism is at an all-time high (ATH) and millions of American people choose to “identify” as young, rich, and very beautiful. In Africa, the oil is nearly gone, but the poverty still stinks. The energy transition is still mostly a theoretical idea, alternations between mild heat waves and raging rainstorms visit everyone who lives in the tropics. The Cold War between Russia and the USA, started in 1947, still goes on. Occasionally becoming hot every other while, to the detriment of other people. This is a ystopia. D At least burgers are still cheap (they contain insects now), and many AI data servers have been relocated to the moon to allow human beings some peace and quiet. These servers would be buzzing all over the place like bees (that don’t make honey). Unsurprisingly, Bitcoiners have decided they need another freedom technology on their hands. Bitcoin is being milked by AI, for AI ensures taxes are paid in lossy inflationary dollars. Nobody still thinks the bubble will burst. Maybe the can be kicked as far into the future as our imaginations allow us. can Bitcoiners learn about quantum computing big time. And underground start-ups are started. Quantum Hacking the World’s Problems A Short Story It is Monday 15th March, 2100. Finally, the password-cracking monster is created. Call it QKraken. The scientist responsible for this works for a pretty huge company under the ground, off books, and off signals. Code name - Q After cranking in parallel universes for 8 hours, the QC spits out the key to one of Satoshi's earliest wallet addresses. There they are - 50BTC. 500 million dollars. He almost pees his pants. "Should I call HQ?" HQ is sleeping. Nobody works past midnight or this early on a Monday. It won’t be rewarded (damned ballooning taxes). So, 8 hours of work on Monday, in the 22nd century, is enough work as it is. "Should I freaking spend some??" He tries to think, but it is not happening. SHA256 and RSA are now toast! His team can finally crack open decades of secrets shored up by millions of people, over the course of decades! Jesus. This is nuclear! Q tries to think but it is not happening. Computing game theoretic analyses that would make a supercomputer freeze out, he falls asleep at the computer. And he has this strange dream: It is Judgement Day, like in the Terminator movies. Someone, perhaps the Russians, get tired of the monolithic power wielded by the US Dollar and its huge AI dogs, so one of them presses the button. Or is it the Joker from the Batman movies? The countdown is started, and nobody is around who remembers how to press the kill switch. This is it. This is the Great Filter. Fate is going to reset the clock. Take human civilization back to the Stone Age. The nukes fall fast and in the thousands. Nothing can stop them. In a few minutes, their parabolic trajectories will be through. Then, hell will engulf the earth. They all fall harmlessly to the ground. Like dead weights. Why, what happened? … “Q” He wakes up. A little frightened. “You fell asleep at your desk, are you alright?” “Yes…yes, I’m alright” - wiping slight drool from his mouth. Vivian looks at him with pitiful eyes. Q isn’t sure if he even cracked SHA256. He wants to check again, but he is too tired. Did he stop a nuclear fallout? Did he destroy encryption? He decides to go out and check. Forgetting he could just turn on his phone and check if WhatsApp is no longer encrypted. He gets into the street. It is a usual Monday morning. Nothing unusual. He needs a break. He goes home. There is yet enough time to save the world. Of that, he feels sure. *** Shor-GPT In a good world, people will want Shor-GPT (a spin-off of ChatGPT). For they are accruing massive amounts of files and accounts whose passwords they never remember. *** Being a Bitcoiner who loves quantum computers, is like being a nuclear scientist who loves world peace. At the surface, it is impossible. Then you dig a little deeper and behold, only nuclear power shall cure energy poverty and the cycles of currency inflation rooted in playing the economy like a fiddle (because more useful energy, not more money, creates value in an economy). If anything, we should perfect playing the atom like a fiddle, not fellow humanity. It is kind of the same thing with quantum computers. Yeah yeah, Shor’s algorithm might “one day” hack Bitcoin’s encryption algorithms. Until you realize quantum computers are too at the moment. busy kicking their own butts . Like Spot But given that Shor’s algorithm (with its logarithmic speed) is stronger than noble quantum algorithms like Grover’s search which could be used for Bitcoin mining (using only quadratic speed), then the reality of hacking encryption is something we need to find good and profitable uses for else it will remain simply destructive. The first thing is the need, and the second is the cost. Even today, encryption can be hacked at many levels if you have a need and can provide the cost. Say you lose the password to your laptop, hacking it is a good thing. Say you lose the password to your phone with $50 million worth of BTC; hacking it is a good thing. You don’t care if the process will use a CPU or a QPU or a prayer; you want to open the phone. This is ethical hacking. Like crime, unethical hacking spreads when social structures become oppressive to their citizens. Massive debt burdens - like $100 trillion - are oppressive. So the drug use, the crime, and the unethical hacking will only get worse. Whether it be with classical, quantum, or social hacking technologies. The shabbiness will get worse, and our only hope is to take up laptops and build classical, quantum, social code. If somebody doesn’t want nefarious characters who build selfish quantum hacking tech, then they should not frustrate plus oppress their poor parents while these characters are still children. So flip it on its head, and it is a glorious revolution. Hacking, once again, becomes discovery. It is discovery. We hack away rocks and find gold, oil, and fossilized dinosaur bones. Hacking is breaking new ground. Adult humans become alike little children, breaking open their toys to see what makes them tick. So that they can buidl even better toys. (Aside - This is why I destroyed many of my childhood toy cars). “Truly I tell you, unless you change and become like little children, you will never enter the kingdom of heaven” - Matthew 18:3 (Holy Bible) Heaven, Utopia, awaits us if we change our ways. Awaits us if we acknowledge that ice and fire, Bitcoin and Quantum Computers, are no enemies. They are two great creatures with great use cases. And we need to tame them both. Or they will tame us.

A Tale of Ice and Fire

Dust on Crust Part Deux

ভূত্বক অংশ দুই উপর ধুলো

Staub auf der Kruste Teil Zwei

Polvo sobre la corteza, segunda parte

पपड़ी पर धूल भाग दो

クラスト上のダスト パート 2

지각 위의 먼지 2부

Poeira na Crosta Parte Dois

Пыль на корке, часть вторая

Kabuktaki Toz İkinci Bölüm

Bụi trên lớp vỏ Phần thứ hai

地壳上的灰尘第二部分

I’ve been writing code for 20+ years, and along the way, I’ve written a ton of code that I needed to go back and refactor. It happens. In fact, it’s almost guaranteed if you want to keep a codebase alive as you’re evolving it. As a result, I’ve done a lot of r C# code and want to share with you some simple refactoring techniques that can be especially useful if you’re just getting started. efactoring 1 – Extract Method Refactoring code is an important skill for software engineers, as it helps improve the readability and maintainability of our codebase. One such technique that can greatly assist us in achieving this goal is the Extract Method technique. I use this so frequently that almost immediately after I write some code I do this! The Concept of Extract Method The Extract Method technique involves taking a block of code within a method and extracting it into a separate method. By doing this, we can improve the clarity and understandability of our code by breaking it down into smaller, more manageable pieces. Don’t wait until you have a 2000-line method before you figure out why this is helpful! To demonstrate this technique, consider the following example in C#: public class Calculator
{
    public int Add(int a, int b)
    {
        Console.WriteLine($"Adding {a} and {b}...");
        int result = a + b;
        Console.WriteLine("The result is: " + result);
        return result;
    }
} In the above code, the method is responsible for: Add Printing out the calculation info Performing the addition operation Displaying the result It’s a pretty simple method, but it’s doing a handful of things already. By applying the Extract Method technique, we can separate the concerns and create a new method solely responsible for displaying info. public class Calculator
{
    public int Add(int a, int b)
    {
        DisplayOperationInfo(a, b);
        int sum = a + b;
        DisplaySum(sum);
        return sum;
    }

    private void DisplayOperationInfo(int a, int b)
    {
        // TODO: could we go even further and pass in
        // the operation string?!
        Console.WriteLine($"Adding {a} and {b}...");
    }

    private void DisplayResult(int result)
    {
        Console.WriteLine("The result is: " + result);
    }
} In this particular case, this technique is not helping us reduce the duplication of existing code (right now), but it is helping us bring some organization to the code. Given that this is a very trivial example, the real benefit of this is hard to observe — I mean, I added more code overall to make this happen. However, I’m trying to illustrate that we can conceptually group code into methods for better expression of what we’re trying to achieve. It’s just hard to show super simple examples of this! Benefits of Extract Method Using the Extract Method technique provides several benefits. Firstly, it improves the readability of our code by creating smaller, self-explanatory methods that focus on a single responsibility. This makes it easier for other developers to understand and maintain the code. This is what I tried to illustrate in the example code prior. Secondly, it promotes code reusability. By extracting a block of code into a separate method, we can easily reuse it in multiple places within our codebase. This reduces duplication and enforces the DRY (Don’t Repeat Yourself) principle. If we go to add other operators to our calculator, we could re-use the DisplayResult method and even refactor the DisplayOperationInfo method to pass in a parameter for which operation we’re performing! Scenarios Where Extract Method is Useful The Extract Method technique is particularly useful in scenarios where: A block of code within a method is becoming too long and complex – By extracting the code into a separate method, we can make the original method more concise and easier to understand. Common functionality needs to be reused across different methods – Extracting the common functionality into a separate method promotes code reuse and eliminates duplication. A method is responsible for multiple concerns – By extracting specific parts of the method into separate methods, we can improve the clarity and maintainability of the code. The Extract Method technique that once you start using, it almost becomes second nature in how you’re coding. By breaking down complex methods into smaller, focused ones, we can create code that is easier to understand and maintain. technique is a fundamental refactoring 2 – Rename Variables and Methods Clear and descriptive names are important in software engineering as they enhance code readability and maintainability. We spend so much of our time as programmers reading code that it’s critical we understand how to write expressive and concise code. When naming variables and methods, it’s important to choose names that accurately reflect their purpose and functionality. But guess what? Things change and evolve. The awesome name you picked today might not be so awesome 12 months from now when you’re trying to read your code and understand what the heck is going on. That’s cool because not only does it show you’re growing and improving, but because we have tools that can make it easy for us to make such changes. Importance of Clear and Descriptive Names Naming variables and methods with clear and descriptive names enables other developers to quickly understand their purpose and how they are used. This not only improves the readability of the code but also makes it easier to maintain and debug. Consider the following example: int x = 10;
int y = 5;
int z = x + y; In this code snippet, it is not immediately clear what the purpose of the variables , , and is. By renaming the variables to more descriptive names, we can make the code more self-explanatory: x y z int dollarsFromJack = 10;
int dollarsFromJill = 5;
int totalSpendingMoney = dollarsFromJack + dollarsFromJill; Now, it is clear that and represent the numbers being added, and represents their result — AND we get some context for why we’re using these numbers in the first place. dollarsFromJack dollarsFromJill totalSpendingMoney Using Visual Studio to Rename When renaming variables and methods, it is important to follow a consistent naming convention. In C#, the common practice is to use PascalCase for method names and camelCase for variable names. But what happens if you picked a crappy name? What happens if you forgot about the naming conventions and need to go update the usage of something in 100 spots? In Visual Studio, we have access to a rename refactoring shortcut. By default, this is F2 on your keyboard. Simply: Highlight your variable that you want to rename Press F2 Type in your new awesome name Press enter … Profit! You can repeat this for many things like classes, interfaces, method names… You name it! (pun intended?) Renaming things is one of the safest types of refactoring that we can do *IF* (and this is a REALLY BIG IF) you are renaming across all of the areas that are consuming your code. So for really small projects, especially if you don’t any types by name or heavily use reflection, you’re probably okay if there’s nobody consuming your library. But if you’re creating a public API or otherwise can’t update all of the consuming code paths at the same time you’re renaming – You’re probably going to break things. dynamically load ! I do earn money from this course, and this is an affiliate link should you choose to purchase other courses on Dometain. Thank you for your support! Click here to get this course Naming Conventions and Best Practices Naming conventions are going to come down to stylistic choices that may vary from team to team. However, there’s handful of general tenets that apply when considering how you name: Use descriptive names that accurately reflect the purpose of the variable or method. Avoid using single-character or ambiguous names, as they can make the code harder to understand. Consider using nouns for variables and verbs for methods to make their purpose clear. Avoid using reserved words or common language constructs as names. Consider reducing redundancy in your names. Remember to discuss with your team about what things they value in naming and what styles they adhere to. 3 – Extract Interface The Extract Interface refactoring technique is another thing that I rely on heavily in my own development. It involves creating an interface that defines a contract for a certain set of methods or properties, and then extracting that interface from existing code. This technique can help increase modularity, improve code reusability, and enhance testability. I generally start with the class itself so I can focus on what I’ll need passed in and what I can provide back out, which I find helps me come up with a better API contract for the interface. Let’s consider an example where we have a class called that performs various mathematical operations: Calculator public class Calculator
{
    public int Add(int a, int b)
    {
        return a + b;
    }

    public int Subtract(int a, int b)
    {
        return a - b;
    }

    public int Multiply(int a, int b)
    {
        return a * b;
    }

    public int Divide(int a, int b)
    {
        return a / b;
    }
} In this case, we can extract an interface, let’s call it , which will define the contract for these mathematical operations: ICalculator public interface ICalculator
{
    int Add(int a, int b);

    int Subtract(int a, int b);

    int Multiply(int a, int b);

    int Divide(int a, int b);
} Once we have the interface, we can modify our class to implement it: Calculator public class Calculator : ICalculator
{
    public int Add(int a, int b)
    {
        return a + b;
    }

    public int Subtract(int a, int b)
    {
        return a - b;
    }

    public int Multiply(int a, int b)
    {
        return a * b;
    }

    public int Divide(int a, int b)
    {
        return a / b;
    }
} Now, instead of directly using the class, we can program to the interface by using : Calculator ICalculator public void Calculate(ICalculator calculator)
{
    int result = calculator.Add(5, 3);
    // Do something with the result
} And yes – I know this is a trivial example and an interface for this is likely overkill. However, remember that calculator I included at the beginning of these examples? The one that writes to the console? Yeah, if you wanted to swap that out for one that wasn’t blowing up your console output then you could at least! But in all seriousness, this is just to illustrate to beginners that the interface defines an API and the class has the implementation. Advantages of Extracting Interfaces There are several advantages to using interfaces in software design and development: Promote loose coupling between components – By programming to an interface rather than a specific implementation, you can easily swap out different implementations without affecting the rest of the codebase. This promotes flexibility and extensibility. Code reusability – By defining a contract through an interface, you can create multiple classes that implement that interface. This allows for the reuse of code across different classes and reduces duplication. Improve testability – With interfaces, you can easily create mock objects or stubs for testing purposes. This makes it simpler to write unit tests and verify the behavior of your code in isolation. When should you use the Extract Interface technique? It’s particularly useful when you have multiple classes that share a common set of methods or properties. By extracting an interface, you can ensure consistency among these classes and simplify their usage. Of course, if you program like me, you potentially over-use interfaces. Sometimes there’s no real benefit to slapping an interface onto a class. Truly. I’m the kind of person who likes to mock any dependency when I unit test (I have other tests for real dependencies), and I *still* don’t think it makes sense to use an interface with everything. So use your judgement! 4 – Simplify Conditional Expressions When writing code, it’s important to have clear and concise conditional expressions. Complicated conditionals can lead to confusion and make code harder to read and maintain. When conditionals are too big, they’re hard to understand. When conditionals are too compact with poorly-named acronym or single-character variables, they’re hard to understand. So strike the right balance of verbosity to get your intention expressed clearly. Use the Ternary Operator: The ternary operator is a concise way to write simple conditional expressions. It has the form . By using the ternary operator, you can replace if-else statements with a single line of code. condition ? trueExpression : falseExpression Here’s an example: bool isEven = (number % 2 == 0) ? true : false; What’s not a fun time? Stacking multiple of these together. Try to avoid that. Extract Complex Conditions into Separate Boolean Variables: If you have complex conditions with multiple logical operators, it can be challenging to understand their meaning at a glance. To improve readability, consider extracting the conditions into separate boolean variables with descriptive names. Here’s an example: bool hasEnoughStock = (quantity > 0 && stockCount > 0);
bool isEligibleForDiscount = (customerAge >= 60 || isEmployee);
if (hasEnoughStock && isEligibleForDiscount)
{
    // Apply discount logic here
} This code is much easier to read than trying to put all four conditions directly into the if statement, especially because we get added context with clear variable names! Use the Null-Conditional Operator: The null-conditional operator ( ) is a handy feature introduced in C# 6.0. It allows you to safely access members of an object, even if the object itself is null. This operator can simplify the null-checking code by avoiding unnecessary statements. ?. if Here’s an example: // Will be null if 'text' is null
int? length = text?.Length; This one is good to discuss with your teams first. The shorthand here may not be obvious to folks, especially if they’re not used to this syntax in the code base. Wrapping Up Refactoring C# Code In this article, I’ve covered 4 important C# code techniques for refactoring Extract Method Rename Variables and Methods Extract Interface Simplify Conditional Expressions These techniques are important for software engineers looking to improve the readability, maintainability, and overall quality of their code. Remember, refactoring code is not just about improving the aesthetics, but about making it easier to work with and maintain in the long run. If you found this useful and you’re looking for more learning opportunities, consider and check out my ! subscribing to my free weekly software engineering newsletter free videos on YouTube Also published . here

4 Essential Techniques Simplified for Refactoring C# Code

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