Quaro ist das Keyword-Recherche-Tool für das ganze Unternehmen mit dem jeder Mitarbeiter selbstständig recherchieren kann. Durch die Abbildung des vollständigen Workloads einer Keyword-Recherche im Tool, kann auf Excel und KW-Planner vollständig verzichtet werden. So können Keyword-Recherchen ohne viel Aufwand und zeitsparend erstellt werden. Quaro hält angelegte Recherchen cloudbasiert aktuell, bearbeitet diese nach und bereitet sie grafisch auf.

Quaro

quaro

**SHERMAN OAKS, United States, May 14th, 2024/GamingWire/--**Xsolla, a global video game commerce company, published the Spring 2024 edition of "The Xsolla Report: The State of Play” today. Launched on the heels of the Game Developers Conference (GDC) 2024, this extensive report provides invaluable insights into the emerging trends and pivotal shifts impacting the gaming industry in the short and long term.  It sets the stage to significantly shape the future of mobile gaming, deepen academic connections within the gaming ecosystem, and redefine investment patterns. In an era where mobile gaming commanded a 49% share of the global market in 2023, this edition of "The State of Play" sheds light on the evolving landscape of mobile gaming. The report navigates through the advancements in global compliance and regulation, including the Digital Markets Act in Europe's new player engagement strategies, offering a glimpse into the potential future of mobile gaming monetization and distribution. It discusses the impact of cross-platform play and the importance of innovative monetization models, providing actionable insights for developers and industry stakeholders. "The State of Play" emphasizes the importance of academia in the growth and diversification of the gaming industry. It explores how educational programs and initiatives develop talent and promote diversity and inclusivity within the gaming community. This edition underlines the symbiotic relationship between the gaming industry and academic institutions, highlighting programs that significantly impact students and the industry. Berkley Egenes, Chief Marketing and Growth Officer at Xsolla, comments: "As we introduce the latest edition of 'The State of Play,' we're not merely sharing industry insights but advocating for a transformative vision: Equal Access for Everyone. This initiative goes beyond our commitment to innovation and growth within the gaming industry. It's about breaking down barriers to ensure that every developer, regardless of company size, has the opportunity to showcase their creativity and reach a global audience. Our focus is on providing platforms and tools that foster creative opportunities, international exposure, and the development of unique projects, ensuring that the future of gaming is accessible to all. We aim to empower every player and developer worldwide, ensuring the gaming landscape is as diverse and dynamic as its community." The report offers an in-depth analysis of the gaming industry's current investment climate, including funding trends, mergers, and acquisitions. It outlines the shifts in investment patterns, from the heights of the pandemic-induced boom to a more measured approach in 2024. "The State of Play" provides a roadmap for navigating the industry's financial aspects, offering insights into strategic investment opportunities and forecasting future trends. Featuring expert commentary from industry voices like Mukul Aurora, Co-founder of Appsoleut Games; Mariusz Gasiewski, CEO of Mobile Gaming and Apps Lead at Google; Karla Reyes, Founder and Studio Director, Anima Interactive, the Xsolla Report: The State of Play is now available for complimentary download. To secure your copy and gain invaluable insights into the gaming industry, visit our website:  xsolla.pro/txr-spring24 Contact PR Director Derrick Stembridge Xsolla d.stembridge@xsolla.com 919-971-7855 This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SHERMAN OAKS, United States, May 14th, 2024/GamingWire/--**Xsolla, a global video game commerce company, published the Spring 2024 edition of "The Xsolla Report: The State of Play” today. Launched on the heels of the Game Developers Conference (GDC) 2024, this extensive report provides invaluable insights into the emerging trends and pivotal shifts impacting the gaming industry in the short and long term.  It sets the stage to significantly shape the future of mobile gaming, deepen academic connections within the gaming ecosystem, and redefine investment patterns. In an era where mobile gaming commanded a 49% share of the global market in 2023, this edition of "The State of Play" sheds light on the evolving landscape of mobile gaming. The report navigates through the advancements in global compliance and regulation, including the Digital Markets Act in Europe's new player engagement strategies, offering a glimpse into the potential future of mobile gaming monetization and distribution. It discusses the impact of cross-platform play and the importance of innovative monetization models, providing actionable insights for developers and industry stakeholders. "The State of Play" emphasizes the importance of academia in the growth and diversification of the gaming industry. It explores how educational programs and initiatives develop talent and promote diversity and inclusivity within the gaming community. This edition underlines the symbiotic relationship between the gaming industry and academic institutions, highlighting programs that significantly impact students and the industry. Berkley Egenes, Chief Marketing and Growth Officer at Xsolla, comments: "As we introduce the latest edition of 'The State of Play,' we're not merely sharing industry insights but advocating for a transformative vision: Equal Access for Everyone. This initiative goes beyond our commitment to innovation and growth within the gaming industry. It's about breaking down barriers to ensure that every developer, regardless of company size, has the opportunity to showcase their creativity and reach a global audience. Our focus is on providing platforms and tools that foster creative opportunities, international exposure, and the development of unique projects, ensuring that the future of gaming is accessible to all. We aim to empower every player and developer worldwide, ensuring the gaming landscape is as diverse and dynamic as its community." The report offers an in-depth analysis of the gaming industry's current investment climate, including funding trends, mergers, and acquisitions. It outlines the shifts in investment patterns, from the heights of the pandemic-induced boom to a more measured approach in 2024. "The State of Play" provides a roadmap for navigating the industry's financial aspects, offering insights into strategic investment opportunities and forecasting future trends. Featuring expert commentary from industry voices like Mukul Aurora, Co-founder of Appsoleut Games; Mariusz Gasiewski, CEO of Mobile Gaming and Apps Lead at Google; Karla Reyes, Founder and Studio Director, Anima Interactive, the Xsolla Report: The State of Play is now available for complimentary download. To secure your copy and gain invaluable insights into the gaming industry, visit our website: xsolla.pro/txr-spring24 xsolla.pro/txr-spring24 xsolla.pro/txr-spring24 Contact PR Director Derrick Stembridge Xsolla d.stembridge@xsolla.com 919-971-7855 This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Xsolla Releases Quarterly Insights Report On The Future Of Gaming And Game Development

Headset Reality Wars: Meta @ $1B Quarterly VR Revenue & Apple Vision Pro Sells 200k Units in 10 Days

হেডসেট রিয়ালিটি ওয়ারস: মেটা @ $1B ত্রৈমাসিক VR আয় এবং Apple Vision Pro 10 দিনে 200k ইউনিট বিক্রি করে

Headset Reality Wars: Meta erzielt vierteljährlichen VR-Umsatz von 1 Milliarde US-Dollar und Apple Vision Pro verkauft 200.000 Einheiten in 10 Tagen

Headphone Reality Wars: Meta con ingresos trimestrales de realidad virtual de 1.000 millones de dólares y Apple Vision Pro vende 200.000 unidades en 10 días

Headphone Reality Wars : Meta @ 1 milliard de dollars de revenus VR trimestriels et Apple Vision Pro vend 200 000 unités en 10 jours

हेडसेट रियलिटी वॉर्स: मेटा @ $1बी त्रैमासिक वीआर राजस्व और ऐप्पल विज़न प्रो ने 10 दिनों में 200k इकाइयाँ बेचीं

ヘッドセット リアリティ ウォーズ: メタ @ 四半期 VR 収益 10 億ドル、Apple Vision Pro は 10 日間で 20 万台を販売

헤드셋 현실 전쟁: Meta @ $1B 분기별 VR 수익 및 Apple Vision Pro, 10일 만에 20만 대 판매

Headset Reality Wars: Meta @ receita trimestral de VR de US$ 1 bilhão e Apple Vision Pro vende 200 mil unidades em 10 dias

Войны реальности гарнитур: Meta с ежеквартальной выручкой в 1 миллиард долларов и Apple Vision Pro продает 200 тысяч единиц за 10 дней

Kulaklık Gerçeklik Savaşları: Meta @ 1 Milyar Dolar Üç Aylık VR Geliri ve Apple Vision Pro 10 Günde 200 Bin Birim Sattı

Cuộc chiến thực tế về tai nghe: Meta @ $1B Doanh thu VR hàng quý & Apple Vision Pro bán được 200 nghìn chiếc trong 10 ngày

耳机现实大战：Meta @ 10 亿美元季度 VR 收入 & Apple Vision Pro 10 天内售出 20 万台

QUARTZ FIBERS

It's been a busy few weeks, with each of the tech behemoths reporting their earnings for the third quarter of the year. Here's a quick recap of the billions each one of them made: -parent saw a whopping 11% year-over-year in its revenue during the three months ended Sept. 30. The tech titan made a profit of $19.69 billion on revenues of $76.69 billion in the preceding quarter, compared to a profit of $13.91 billion on revenues of $69.09 billion in the same period of 2022. Google Alphabet increase The increases were driven by "meaningful growth in Search and YouTube, and momentum in Cloud," Alphabet CFO Ruth Porat said in a statement. too reported an in its revenues and profit for the three months ended Sept. 30. The Windows maker generated $56.52 billion in revenue and $22.29 billion in income last quarter, compared to the $50.12 billion in revenue and $17.56 billion in net income in the same quarter last year. Microsoft increase As with Google, Microsoft attributed a momentum in cloud computing as a catalyst for the increased sales. Meanwhile, over at Mark Zuckerberg's Meta, the Menlo Park, Calif.-based social media giant had an eye popping . The company made $34.15 billion in revenue, up 23% year over year, but the breakthrough was of course, the profit: up more than 2.5x to $11.58 billion from the $4.40 billion a year ago. turnaround story Topping things off, said its net sales increased 13% year over year to $143.1 billion during the most recent quarter while net income was to $9.9 billion from $2.9 billion a year ago. Amazon up Despite raking in billions, each of these companies of people this year in a bid to save cost and even out the lack of growth from the COVID-19 days when their businesses were booming. laid off thousands Google ranked #6 on HackerNoon's Tech Company Rankings this week. Microsoft ranked #8 while Meta was on the #24 spot. ⛲ Amazon, Google's Plan to Beat ChatGPT: Invest in Its Competitor It looks like Google is partnering with Amazon in funding a startup that promises to compete head to head with Microsoft-backed OpenAI's ChatGPT. We are, of course, talking about Anthropic, the San Francisco-based generative AI startup that received an inflow of $1.25 billion from Amazon last month with promises of another cool $2.75 billion. Well, Anthropic is now Google to its list of investors, as the search giant too is investing a cool half a billion dollars in the startup with commitments of up to $1.5 billion in the future. adding The combined $6 billion from Amazon and Google, while nothing to scoff at, is still less than half the $13 billion Microsoft has reportedly into OpenAI ever since it shook hands with the company in 2019. injected What's also interesting is the fact that, as opposed to OpenAI, Anthropic has garnered interest from two major tech giants who compete against each other in the cloud space. It just might seem that companies that are lagging behind in the AI space are trying to play catchup and catch lightning in a bottle, similar to how Microsoft did with its investment in OpenAI. Also, an interesting fact: Anthropic was co-founded by former OpenAI executives and siblings Dario and Daniela Amodei. Exciting times ahead! 👋 You’re reading of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe to receive the newsletter in your inbox every Tuesday. part 2 Part 1 here complete In Other News.. 📰 Resident Evil Village on the iPhone could be a preview of Apple’s gaming future — via . The Verge Google CEO Sundar Pichai to testify in US antitrust trial — via . Reuters Amazon’s Tye Brady discusses generative AI, humanoid robots and mobile manipulation — via . TechCrunch A look back at Elon Musk’s first year owning Twitter, in tweets — via . CNN Fidelity has marked down the value of Twitter/X by 65% — via . Axios Biden unveils U.S. government’s first-ever AI executive order — via . CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's page. Tech Company Rankings

The Tech Company News Brief: How Much Money Did Tech Companies Make Last Quarter? 

টেক কোম্পানির সংবাদ সংক্ষিপ্ত: টেক কোম্পানিগুলো গত ত্রৈমাসিকে কত টাকা উপার্জন করেছে?

The Tech Company News Brief: Wie viel Geld haben Technologieunternehmen im letzten Quartal verdient?

Resumen de noticias de The Tech Company: ¿Cuánto dinero ganaron las empresas de tecnología el último trimestre?

The Tech Company News Brief : Combien d’argent les entreprises technologiques ont-elles gagné au dernier trimestre ?

टेक कंपनी समाचार संक्षिप्त: टेक कंपनियों ने पिछली तिमाही में कितना पैसा कमाया?

テクノロジー企業ニュース概要: テクノロジー企業は前四半期にどれくらいの利益を上げましたか?

기술 회사 뉴스 개요: 기술 회사는 지난 분기에 얼마나 많은 돈을 벌었나요?

Resumo das notícias da The Tech Company: Quanto dinheiro as empresas de tecnologia ganharam no último trimestre?

Краткий обзор новостей технологических компаний: сколько денег технологические компании заработали в прошлом квартале?

Tech Company Haber Özeti: Teknoloji Şirketleri Son Çeyrek Ne Kadar Para Kazandı?

Bản tin tóm tắt về công ty công nghệ: Các công ty công nghệ đã kiếm được bao nhiêu tiền trong quý vừa qua?

科技公司新闻简报：科技公司上季度赚了多少钱？

Phineas Redux by Anthony Trollope, is part of the HackerNoon Books Series. You can jump to any chapter in this book . WHAT CAME OF THE QUARREL. here WHAT CAME OF THE QUARREL. On the next morning at seven o'clock a superintendent of police called at the house of Mr. Gresham and informed the Prime Minister that Mr. Bonteen, the President of the Board of Trade, had been murdered during the night. There was no doubt of the fact. The body had been recognised, and information had been taken to the unfortunate widow at the house Mr. Bonteen had occupied in St. James's Place. The superintendent had already found out that Mr. Bonteen had been attacked as he was returning from his club late at night,—or rather, early in the morning, and expressed no doubt that he had been murdered close to the spot on which his body was found. There is a dark, uncanny-looking passage running from the end of Bolton Row, in May Fair, between the gardens of two great noblemen, coming out among the mews in Berkeley Street, at the corner of Berkeley Square, just opposite to the bottom of Hay Hill. It was on the steps leading up from the passage to the level of the ground above that the body was found. The passage was almost as near a way as any from the club to Mr. Bonteen's house in St. James's Place; but the superintendent declared that gentlemen but seldom used the passage after dark, and he was disposed to think that the unfortunate man must have been forced down the steps by the ruffian who had attacked him from the level above. The murderer, so thought the superintendent, must have been cognizant of the way usually taken by Mr. Bonteen, and must have lain in wait for him in the darkness of the mouth of the passage. The superintendent had been at work on his inquiries since four in the morning, and had heard from Lady Eustace,—and from Mrs. Bonteen, as far as that poor distracted woman had been able to tell her story,—some account of the cause of quarrel between the respective husbands of those two ladies. The officer, who had not as yet heard a word of the late disturbance between Mr. Bonteen and Phineas Finn, was strongly of opinion that the Reverend Mr. Emilius had been the murderer. Mr. Gresham, of course, coincided in that opinion. What steps had been taken as to the arrest of Mr. Emilius? The superintendent was of opinion that Mr. Emilius was already in custody. He was known to be lodging close to the Marylebone Workhouse, in Northumberland Street, having removed to that somewhat obscure neighbourhood as soon as his house in Lowndes Square had been broken up by the running away of his wife and his consequent want of means. Such was the story as told to the Prime Minister at seven o'clock in the morning. At eleven o'clock, at his private room at the Treasury Chambers, Mr. Gresham heard much more. At that time there were present with him two officers of the police force, his colleagues in the Cabinet, Lord Cantrip and the Duke of Omnium, three of his junior colleagues in the Government, Lord Fawn, Barrington Erle, and Laurence Fitzgibbon,—and Major Mackintosh, the chief of the London police. It was not exactly part of the duty of Mr. Gresham to investigate the circumstances of this murder; but there was so much in it that brought it closely home to him and his Government, that it became impossible for him not to concern himself in the business. There had been so much talk about Mr. Bonteen lately, his name had been so common in the newspapers, the ill-usage which he had been supposed by some to have suffered had been so freely discussed, and his quarrel, not only with Phineas Finn, but subsequently with the Duke of Omnium, had been so widely known,—that his sudden death created more momentary excitement than might probably have followed that of a greater man. And now, too, the facts of the past night, as they became known, seemed to make the crime more wonderful, more exciting, more momentous than it would have been had it been brought clearly home to such a wretch as the Bohemian Jew, Yosef Mealyus, who had contrived to cheat that wretched Lizzie Eustace into marrying him. As regarded Yosef Mealyus the story now told respecting him was this. He was already in custody. He had been found in bed at his lodgings between seven and eight, and had, of course, given himself up without difficulty. He had seemed to be horror-struck when he heard of the man's death,—but had openly expressed his joy. "He has endeavoured to ruin me, and has done me a world of harm. Why should I sorrow for him?"—he said to the policeman when rebuked for his inhumanity. But nothing had been found tending to implicate him in the crime. The servant declared that he had gone to bed before eleven o'clock, to her knowledge,—for she had seen him there,—and that he had not left the house afterwards. Was he in possession of a latch-key? It appeared that he did usually carry a latch-key, but that it was often borrowed from him by members of the family when it was known that he would not want it himself,—and that it had been so lent on this night. It was considered certain by those in the house that he had not gone out after he went to bed. Nobody in fact had left the house after ten; but in accordance with his usual custom Mr. Emilius had sent down the key as soon as he had found that he would not want it, and it had been all night in the custody of the mistress of the establishment. Nevertheless his clothes were examined minutely, but without affording any evidence against him. That Mr. Bonteen had been killed with some blunt weapon, such as a life-preserver, was assumed by the police, but no such weapon was in the possession of Mr. Emilius, nor had any such weapon yet been found. He was, however, in custody, with no evidence against him except that which was afforded by his known and acknowledged enmity to Mr. Bonteen. So far, Major Mackintosh and the two officers had told their story. Then came the united story of the other gentlemen assembled,—from hearing which, however, the two police officers were debarred. The Duke and Barrington Erle had both dined in company with Phineas Finn at Madame Goesler's, and the Duke was undoubtedly aware that ill blood had existed between Finn and Mr. Bonteen. Both Erle and Fitzgibbon described the quarrel at the club, and described also the anger which Finn had expressed against the wretched man as he stood talking at the club door. His gesture of vengeance was remembered and repeated, though both the men who heard it expressed their strongest conviction that the murder had not been committed by him. As Erle remarked, the very expression of such a threat was almost proof that he had not at that moment any intention on his mind of doing such a deed as had been done. But they told also of the life-preserver which Finn had shown them, as he took it from the pocket of his outside coat, and they marvelled at the coincidences of the night. Then Lord Fawn gave further evidence, which seemed to tell very hardly upon Phineas Finn. He also had been at the club, and had left it just before Finn and the two other men had clustered at the door. He had walked very slowly, having turned down to Curzon Street and Bolton Row, from whence he made his way into Piccadilly by Clarges Street. He had seen nothing of Mr. Bonteen; but as he crossed over to Clarges Street he was passed at a very rapid pace by a man muffled in a top coat, who made his way straight along Bolton Row towards the passage which has been described. At the moment he had not connected the person of the man who passed him with any acquaintance of his own; but he now felt sure,—after what he had heard,—that the man was Mr. Finn. As he passed out of the club Finn was putting on his overcoat, and Lord Fawn had observed the peculiarity of the grey colour. It was exactly a similar coat, only with its collar raised, that had passed him in the street. The man, too, was of Mr. Finn's height and build. He had known Mr. Finn well, and the man stepped with Mr. Finn's step. Major Mackintosh thought that Lord Fawn's evidence was—"very unfortunate as regarded Mr. Finn." "I'm d—— if that idiot won't hang poor Phinny," said Fitzgibbon afterwards to Erle. "And yet I don't believe a word of it." "Fawn wouldn't lie for the sake of hanging Phineas Finn," said Erle. "No;—I don't suppose he's given to lying at all. He believes it all. But he's such a muddle-headed fellow that he can get himself to believe anything. He's one of those men who always unconsciously exaggerate what they have to say for the sake of the importance it gives them." It might be possible that a jury would look at Lord Fawn's evidence in this light; otherwise it would bear very heavily, indeed, against Phineas Finn. Then a question arose as to the road which Mr. Bonteen usually took from the club. All the members who were there present had walked home with him at various times,—and by various routes, but never by the way through the passage. It was supposed that on this occasion he must have gone by Berkeley Square, because he had certainly not turned down by the first street to the right, which he would have taken had he intended to avoid the square. He had been seen by Barrington Erle and Fitzgibbon to pass that turning. Otherwise they would have made no remark as to the possibility of a renewed quarrel between him and Phineas, should Phineas chance to overtake him;—for Phineas would certainly go by the square unless taken out of his way by some special purpose. The most direct way of all for Mr. Bonteen would have been that followed by Lord Fawn; but as he had not turned down this street, and had not been seen by Lord Fawn, who was known to walk very slowly, and had often been seen to go by Berkeley Square,—it was presumed that he had now taken that road. In this case he would certainly pass the end of the passage towards which Lord Fawn declared that he had seen the man hurrying whom he now supposed to have been Phineas Finn. Finn's direct road home would, as has been already said, have been through the square, cutting off the corner of the square, towards Bruton Street, and thence across Bond Street by Conduit Street to Regent Street, and so to Great Marlborough Street, where he lived. But it had been, no doubt, possible for him to have been on the spot on which Lord Fawn had seen the man; for, although in his natural course thither from the club he would have at once gone down the street to the right,—a course which both Erle and Fitzgibbon were able to say that he did not take, as they had seen him go beyond the turning,—nevertheless there had been ample time for him to have retraced his steps to it in time to have caught Lord Fawn, and thus to have deceived Fitzgibbon and Erle as to the route he had taken. When they had got thus far Lord Cantrip was standing close to the window of the room at Mr. Gresham's elbow. "Don't allow yourself to be hurried into believing it," said Lord Cantrip. "I do not know that we need believe it, or the reverse. It is a case for the police." "Of course it is;—but your belief and mine will have a weight. Nothing that I have heard makes me for a moment think it possible. I know the man." "He was very angry." "Had he struck him in the club I should not have been much surprised; but he never attacked his enemy with a bludgeon in a dark alley. I know him well." "What do you think of Fawn's story?" "He was mistaken in his man. Remember;—it was a dark night." "I do not see that you and I can do anything," said Mr. Gresham. "I shall have to say something in the House as to the poor fellow's death, but I certainly shall not express a suspicion. Why should I?" Up to this moment nothing had been done as to Phineas Finn. It was known that he would in his natural course of business be in his place in Parliament at four, and Major Mackintosh was of opinion that he certainly should be taken before a magistrate in time to prevent the necessity of arresting him in the House. It was decided that Lord Fawn, with Fitzgibbon and Erle, should accompany the police officer to Bow Street, and that a magistrate should be applied to for a warrant if he thought the evidence was sufficient. Major Mackintosh was of opinion that, although by no possibility could the two men suspected have been jointly guilty of the murder, still the circumstances were such as to justify the immediate arrest of both. Were Yosef Mealyus really guilty and to be allowed to slip from their hands, no doubt it might be very difficult to catch him. Facts did not at present seem to prevail against him; but, as the Major observed, facts are apt to alter considerably when they are minutely sifted. His character was half sufficient to condemn him;—and then with him there was an adequate motive, and what Lord Cantrip regarded as "a possibility." It was not to be conceived that from mere rage Phineas Finn would lay a plot for murdering a man in the street. "It is on the cards, my lord," said the Major, "that he may have chosen to attack Mr. Bonteen without intending to murder him. The murder may afterwards have been an accident." It was impossible after this for even a Prime Minister and two Cabinet Ministers to go about their work calmly. The men concerned had been too well known to them to allow their minds to become clear of the subject. When Major Mackintosh went off to Bow Street with Erle and Laurence, it was certainly the opinion of the majority of those who had been present that the blow had been struck by the hand of Phineas Finn. And perhaps the worst aspect of it all was that there had been not simply a blow,—but blows. The constables had declared that the murdered man had been struck thrice about the head, and that the fatal stroke had been given on the side of his head after the man's hat had been knocked off. That Finn should have followed his enemy through the street, after such words as he had spoken, with the view of having the quarrel out in some shape, did not seem to be very improbable to any of them except Lord Cantrip;—and then had there been a scuffle, out in the open path, at the spot at which the angry man might have overtaken his adversary, it was not incredible to them that he should have drawn even such a weapon as a life-preserver from his pocket. But, in the case as it had occurred, a spot peculiarly traitorous had been selected, and the attack had too probably been made from behind. As yet there was no evidence that the murderer had himself encountered any ill-usage. And Finn, if he was the murderer, must, from the time he was standing at the club door, have contemplated a traitorous, dastardly attack. He must have counted his moments;—have returned slyly in the dark to the corner of the street which he had once passed;—have muffled his face in his coat;—and have then laid wait in a spot to which an honest man at night would hardly trust himself with honest purposes. "I look upon it as quite out of the question," said Lord Cantrip, when the three Ministers were left alone. Now Lord Cantrip had served for many months in the same office as Phineas Finn. "You are simply putting your own opinion of the man against the facts," said Mr. Gresham. "But facts always convince, and another man's opinion rarely convinces." "I'm not sure that we know the facts yet," said the Duke. "Of course we are speaking of them as far as they have been told to us. As far as they go,—unless they can be upset and shown not to be facts,—I fear they would be conclusive to me on a jury." "Do you mean that you have heard enough to condemn him?" asked Lord Cantrip. "Remember what we have heard. The murdered man had two enemies." "He may have had a third." "Or ten; but we have heard of but two." "He may have been attacked for his money," said the Duke. "But neither his money nor his watch were touched," continued Mr. Gresham. "Anger, or the desire of putting the man out of the way, has caused the murder. Of the two enemies one,—according to the facts as we now have them,—could not have been there. Nor is it probable that he could have known that his enemy would be on that spot. The other not only could have been there, but was certainly near the place at the moment,—so near that did he not do the deed himself, it is almost wonderful that it should not have been interrupted in its doing by his nearness. He certainly knew that the victim would be there. He was burning with anger against him at the moment. He had just threatened him. He had with him such an instrument as was afterwards used. A man believed to be him is seen hurrying to the spot by a witness whose credibility is beyond doubt. These are the facts such as we have them at present. Unless they can be upset, I fear they would convince a jury,—as they have already convinced those officers of the police." "Officers of the police always believe men to be guilty," said Lord Cantrip. "They don't believe the Jew clergyman to be guilty," said Mr. Gresham. "I fear that there will be enough to send Mr. Finn to a trial," said the Duke. "Not a doubt of it," said Mr. Gresham. "And yet I feel as convinced of his innocence as I do of my own," said Lord Cantrip. 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. Anthony Trollope (2006). Phineas Redux. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/18640/pg18640-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 , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

WHAT CAME OF THE QUARREL.

Philochristus: Memoirs of a Disciple of the Lord by Edwin Abbott Abbott is part of the HackerNoon Books Series. You can jump to any chapter in this book . How Quartus interpreted the New Law. here How Quartus interpreted the New Law. When Jesus had ended all these words, he came down from the mountain, and we followed, reasoning much among ourselves. Baruch spake first, complaining that the new Law was full of hard sayings. “For,” said he, “when the Prophet proclaimed a blessing on the poor and hungry, that was easy to understand, and I rejoiced thereat: but afterwards, when he bade us bless them that cursed us, and do good to those that injured us, yea, and turn the left cheek to him that had smitten us on the right, and give our coat to him that had taken away our cloak, then indeed his doctrine seemed too wonderful for the mind of man to fathom.” Then a certain Essene (who at that time followed with us) made answer and said, “This world is but as a vestibule before the world to come: therefore the Prophet’s intent is to instruct us how to prepare ourselves at the vestibule so that we may find grace to come into the King’s presence: and his words enjoin on us to abstain from all earthly cares and pleasures, and to withdraw ourselves from the cities of men.” But to this Simon Peter made answer that Jesus had taught us to live in the sight of all men, like a city on an hill or like a candle on a candlestick: moreover, he had promised that we should inherit the earth. But here Eliezer the son of Arak could no longer constrain himself. “I marvel,” he said, “that we listen so long without first asking this Prophet by what authority he sayeth these things, or what sign he can work in Heaven to prove his authority. For other teachers received of teachers before them; as, for example, Hillel and Shammai received from Shemaiah and Abtalion; and Shemaiah and Abtalion received from Jehudah the son of Tabai, and Simeon the son of Shatach; and so on successively; but this teacher maketh mention of no teachers from whom he hath received his doctrine: neither worketh he any sign in Heaven. But whence doth he draw his knowledge about the Unapproachable (blessed is He)? Even from the creatures; even from the weeds of the field, and the silly birds that are caught in the snare of the fowler; from the senseless rain and from the shining of the sun; yea, and from the nature of the heart of man, which is evil from his youth! But how much better than all these is the Law, whereby was created all that is; according as it is said, ‘Beloved are the children of Israel, in that there was given to them the instrument by which the world was created.’ ” No answer was made to the words of Eliezer: but Barabbas took up the words of Baruch, and said, “If we are to turn the left cheek to him which hath smitten the right, and if we are to do good unto them which do us harm, who shall cause injustice to cease in the world? For verily the unjust will wax fat in their injustice and will go on from oppression to oppression.” But said Judas of Kerioth, “Listen unto me, O foolish ones, and take counsel from me: for is it not even as I foretold? Did not I say unto you that the Prophet would not at this time make laws that should endure for ever, but only ordinances for a season, till we had gained the upper hand? Wherefore ye must know that it is in the mind of the Prophet to draw unto himself the hearts of all people by fair words and gentle dealing: but when the time is come for different policy, then we shall take fresh counsel according to our needs. But now hearken. Did not the Prophet prophesy woe to the rich and the powerful? These are the Romans; and in foretelling woe to them, he foretold woe against the Romans. Again, did he not prophesy blessing for the poor? And we are poor: and in every city of Israel the poor are the greater part, and will fight on our side, and will have a part in our blessing. I grant, he said not that we should be judges and princes: but he promised that we should have that for which we asked; and is not this enough for us? Yea, and albeit he mentioned not expressly money, or lands, or houses, yet he said that our reward should be great. But if persecution or the shedding of some of our blood must needs come before our success, who is so fainthearted and womanish as to draw back for such a cause? Therefore, I say, be of good heart; and though there be some dark sayings of the Prophet, let us be content to stand fast on those sayings which are plain. But as touching the words of Eliezer, we all know in our hearts that Jesus is not a man as other men, but that he is a leader sent from God; and howsoever he teacheth, and whithersoever he leadeth, it is our wisdom to obey him and to follow him.” The words of Judas pleased us: and we all agreed to them. Only a certain Alexandrine (whose name was Quartus) said to Baruch that he judged not that the words of Jesus were intended to be merely transitory ordinances. Now this Quartus was a man of no common understanding and discernment; and inasmuch as his father had been a Greek and had caused him to be trained in the Greek learning and philosophy, he spake with more art and subtlety than most of my companions. Howbeit he lacked not faith and the love of righteousness; and, his mother being of our nation, he had been circumcised, and had conformed himself to the worship of Israel; but having been bred up in the schools of the Greeks and in the school of Philo, he was at all times desirous to compare the teaching of other philosophers with the teaching of Jesus. He was a merchant, and his business brought him oftentimes to Capernaum, where I had met him; but I had also met him before in the house of my uncle at Alexandria. So when I overheard Quartus saying these words to my cousin, I questioned him how he interpreted the sayings of Jesus, and in particular, that saying concerning the turning of the cheek to the smiter. Then said Quartus unto me, after some pause, “Be not displeased if I speak in a parable. Many times in Capernaum have I seen mariners (such as know not your waters) grievously tossed by a storm while they strove to enter into the harbour by a straight course, and toiling hard for many hours, but all to no purpose; but others (which know the secret) leave the straight course on one side, and stand far out to Taricheæ. Thence floweth a current toward Capernaum, strong at all times; but in stormy weather it cannot be resisted. Falling into this current therefore, the wise mariner needeth but to row softly, or scarce at all, and lo, he entereth into Capernaum as it were upon wings. Now even such a wise mariner doth Jesus seem unto me.” I marvelled at his words. But Quartus perceived that I understood him not; and he continued, “I speak as one groping in the dark. But the meaning of my parable is this: The lake is the world; the vessel is Israel; and Capernaum is redemption. Other pilots have striven to guide Israel to redemption by dint of force, but they have failed: Jesus is the true pilot, and knoweth the currents and streams in the nature of men and things; and by his wisdom he thinketh to guide us aright.” “But what,” I asked, “are these streams and currents?” Again Quartus was silent for a while, and longer than before, so that by this time we were almost come down from the mountain; but at last he said unto me, “What seemeth to thee the strongest current in the nature of men?” But, when I held my peace, not knowing what to answer, he spake again very earnestly, “Thou art a student of the sayings of the Wise, O Joseph, and canst answer with discerning. Tell me, then, on what standeth the earth?” Then I replied according to the saying, “Upon the pillars; and the pillars upon the waters.” “Yea,” replied Quartus; “and after these cometh the wind; and what after the wind?” Then I said, “Beneath the wind is the storm, and beneath the storm is the arm of the Holy One; for it is said, ‘Underneath are the Everlasting Arms.’ ” Then said Quartus, “It is so; and verily the foundations of earth are the Ever lasting Arms of the Father in Heaven: but if the Fatherhood of God be the strongest thing on earth, and if this be the mightiest stream or current in the nature of men, then how may we best sail with that current?” I remembered the words which Jesus had spoken that we were to become as little children; so I answered, “I suppose, by approaching Him as children.” Here Judas interrupted us and said, “Nay, but wisdom is the strongest thing in the world, for it is written of wisdom, ‘The Lord possessed me in the beginning of His way, before His works of old. When He appointed the foundations of the earth, then I was by Him as one brought up with Him.’ ” “Thou sayest well,” said Quartus, “but what human wisdom is like unto that wisdom which revealeth God to men? Now as no child can understand his father unless he love his father, so no man can know God (who is our Father in Heaven) unless he love Him; but whoso loveth, understandeth; therefore to love God is the highest wisdom of man.” Then Judas scoffed at him and said, “This is nothing but repeating in new words the old saying of the Law, ‘Thou shalt love the Lord thy God with all thy heart, and with all thy soul, and with all thy mind, and with all thy strength.’ What! do ye then deem our Master to be naught but a merchant that retaileth old wares as if they were new?” So he left us and went on before. But Quartus continued, “Judas saith truly that the new Law aimeth at the same mark as the old Law. But the means are diverse. For the old Law worketh by purifications and feasts and sabbaths; but the new Law belike worketh in part by these means, but in greater part by other means. And, as I judge, Jesus goeth toward the end of the old Law; but by a path that is new, yea, altogether new. For I have myself heard him say that the redeemers of old were like unto thieves and robbers, using force and violence; but he himself cometh not like a thief over the wall, but like the true shepherd through the door of the fold, that is to say, through the path of Redemption which God hath appointed. Now this path is kindness or love. And Jesus saith that the former redeemers failed of their purpose, for they thought to redeem men by force; but he will not fail, for he purposeth to redeem men by gentleness. And he saith that God ordaineth strength out of babes and sucklings, and that the spirit of childhood is the conquering spirit of the world. Rememberest thou not how our teacher Philo said some things not much unlike to these, teaching that the highest revelation of God is through love? Howbeit, none methinks, save Jesus only, can reveal this revelation. For Philo testifieth of that which is behind the veil; but Jesus of Nazareth hath power to lift up the veil.” By this time we had overtaken the others, whom we found all sitting, and Jesus in the midst of them. By the side of Jesus was a man bearing in his arms a little child. He was come forth from a house nigh to the place where Jesus sat, bringing a cup of water for Jesus to drink. While Jesus was drinking, the father still kept his eyes upon the child in his arms, and his face was full of compassion and tenderness; for the child was very sickly. We soon perceived that it was the same man that had denied to give us guidance in the morning; but at first we knew him not; pity and love had so transformed his countenance. Now it came to pass that when Jesus had given back the cup to the man, he laid his hands on the child and blessed him. And as he blessed him, his face shone as with the glory of the Lord; and the little one also seemed to rejoice and to partake in the brightness of our Master’s countenance. We both stood still, beholding Jesus. Then said Quartus unto me, “Did not Eliezer the son of Arak say truly, that ‘Jesus looketh upon the book of the world as well as upon the book of the Law, and seeth in all things God’? For even as Elias the Prophet loved to commune with God on the tops of mountains, and in deserts, and in caves, and received revelations of the Lord from earthquakes and fires, but most of all from the still small voice; even so doth our Master look upon all things that are, yea even on the smallest things that live or grow, and from all, he heareth a still small voice that speaketh of the Father. Yea, and there is yet more than this. For whithersoever he turneth his face, methinks he giveth of his love to all things, whether they be the flowers of the field, or the birds, or the mountains, or the children of men: and because he thus giveth, it is given to him again; yea, wisdom and joy and peace are given back to him, even from things that have not life; but most of all from the children of men, which are made in the image of God. Therefore said I that Jesus seemeth as the wise mariner of whom we spake but now; for, by the Word of God in himself, he hath haply hit upon a certain current in the nature of created things, whereby he will easily prevail over the blasts of all opposing storms, and be carried into the haven of God, both he and all they that put their trust in him.” “Thy words are fair,” I replied, “but they do not persuade me. That the love of children doth bind husband and wife together, and that the bond of families is the bond of nations, this I deny not. Perchance also the love that parents have to their children may have lifted up the hearts of many in Israel, during many generations, to the true God. But how we are to take Jerusalem or thrust forth the Romans from Syria by becoming as little children, this passeth my understanding. Or dost thou not believe that Jesus will lead us against the Romans in Jerusalem?” “I know not,” replied Quartus (who spoke as one musing, and not giving heed to my question); “but what troubleth me most of all is the fear lest the knowledge of Jesus may haply perish with him; for if he hath (as I judge that he hath) a certain inborn power of winning men over to his will by kindness and gentleness, then, as it seemeth to me, this power may be likened unto the fabled ring of Solomon, which gave unto the owner well nigh whatsoever he desired. But there is this difference. The ring could be delivered from one man to another; but the art or secret of Jesus is, in all likelihood, not able to be delivered to them that shall come after; but it will perish with him. And then what becometh of his Kingdom of God?” As long as Quartus was speaking I heard him gladly: but when he had ceased, his words seemed like mist in the morning sun; but the words of Judas seemed as the solid ground from which the mist rolleth away. For what Quartus said was hard to understand; but the words of Judas seemed according to reason, and very plain to be understood; to wit, that the ordinances given on the mountain of blessing were transitory, and that we were still to wait for the New Law: and to this I agreed, and not I only, but the most part of the disciples. 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. Edwin Abbott Abbott (2015). Philochristus: Memoirs of a Disciple of the Lord. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/48843/pg48843-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 , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

How Quartus interpreted the New Law.

We’ve all experienced those days when the universe seems to conspire against us. The car won’t start, you spill coffee down your shirt, you botch a high-stakes presentation—and by 10am, you’re ready to wave the white flag on the day. Days like this can feel impossible to salvage. And because we’re only human, when chaos piles up early on, negativity bias convinces us the whole day is already ruined. Our motivation flatlines as we succumb to frustration. But what if you had a framework to compartmentalize setbacks, so they didn't contaminate your entire day? A system to hit mental reset buttons when things veer off track? That's where the 'four-quarter method' comes in. It's a life-changing technique, dividing your day into four distinct parts so you can reset and refocus as needed. It's about building resilience and preventing a rough start from defining your entire day. It forces you to limit the damage when problems arise and structures your time for resilience. Let’s dive in. The Origins of the Four-Quarter Method Like many viral trends, this one traces back to . TikTok I actually came across it while delving into the Eisenhower matrix (the urgent/important framework), and found this to be a more effective and practical alternative that helps you structure your entire day. Creators shared how dividing their day into four ‘quarters’ helped them better manage setbacks. A little deeper research showed that this wasn’t just a TikTok trend, but an emerging tool being adopted across several life optimization and productivity circles. The core idea is to view your waking time not as an amorphous blob, but as four separate segments. Each quarter offers a fresh start—a chance to recalibrate when life in “earlier segments” goes awry. This method provides more runway to absorb mistakes or delays. Rather than a steady grind, your day unfolds through a series of mini-chapters. How to Implement the Four-Quarter System To try it yourself, split your day into four quarters: Morning: ~5am to 9am Late Morning: ~10am to 1pm Afternoon: ~2pm to 7pm Evening: ~7pm onward The exact hours can vary based on your needs. The aim is to create distinct segments that feel meaningfully different from each other. Next, define an intention for each quarter—a guiding focus to orient your activities: Morning: Prepare Late Morning: Produce Afternoon: Polish Evening: Unwind With your quarters mapped out, it’s time to build your daily schedule within this framework. Optimizing the Morning Quarter: Priming for Success Imagine your morning quarter as the on-ramp to launch your day. Use this time to proactively set yourself up for success. Potential morning activities include: Health practices like exercise, hydrating, and nutrition. Reflection such as journaling or meditation to ground yourself in purpose and priorities. Reviewing your schedule and goals. What are 1-3 must-do items? Tackling urgent logistics like responding to pressing emails, calls, or messages. Deal with time-sensitive issues now so they don’t linger. Structuring to-do lists and blocking off time for focus work. The morning is your chance to get into peak state so you can show up powerfully for the day ahead. Curate this time mindfully, rather than defaulting to busy work. Containing Chaos in the Late Morning Quarter With key priorities identified, the late morning is go time. Channel your peak energy levels into driving meaningful progress on substantive work. The late morning is often (or at least it should be) the least interrupted segment of the day. Use it for intense focus on your most cognitively demanding projects. If you find that you are getting interrupted during your late morning, you need to shut off notifications, put your phone away, and block out your calendar from less important calls. Protect this time like your life depends on it (because honestly, it does). You need to architect a distraction-free, needle-moving/ highest priority task, flow state environment. Potential late-morning activities: Your #1 priority task for moving top initiatives forward Deep work time free from distractions Working sessions for brainstorming or strategizing Difficult conversations that require mental sharpness High-stakes presentations, pitches, or speaking engagements The late morning is when you actualize your plans. Convert morning intentions into measurable progress. Regroup in the Afternoon Quarter By mid-afternoon, you might find your energy starting to decrease. Willpower gets depleted and focus suffers. A study in the journal revealed our most accurate decisions happen early in the day, even if we're night owls. Cognition Researchers found chess players made their most precise moves from 8am-1pm, though these took longer. As the day progressed, speed increased but accuracy dropped. Ultimately, faster afternoon decisions compensated for reduced precision, equalizing players' scores. But the trends were clear: mornings bring discernment, afternoons impatience. This data validates the four-quarter method's approach. Use the energized morning quarters for precision work, and leverage faster afternoon thinking for tasks requiring speed. Sync activities to mental strengths at each time. Rigidly powering through a whole day can backfire. Our minds operate in rhythms, alternating intense focus with restoration. Rather than grinding when you’re depleted, the afternoon quarter is a chance to catch your breath. Pause to evaluate what’s working and what needs adjustment. Potential afternoon activities: Completing unfinished tasks from the morning Troubleshooting anything that is stuck or subpar Reassessing priorities in light of new developments Dealing with emerging needs and issues Collaborative meetings to align with team members Administrative work or email catch-up The afternoon offers breathing room between morning intensity and evening recovery. Use it to course correct based on the day’s rhythms. Rest and Recovery in the Evening Quarter In the evening quarter, you’re going to transition your focus from output and accomplishment toward renewal. Protect space for your personal needs and relationships outside of work. Potential evening activities: Exercise or movement Reconnecting with family or friends Hobbies and recreation Relaxing through music, reading, tv, etc. Reflection such as journalling or meditation Guard the evening quarter fiercely for activities unrelated to work. These hours of rest are non-negotiable for maintaining personal well-being and staving off burnout. Set clear boundaries on work activities after hours. Be disciplined about disconnecting from email and messaging in order to fully recharge. Mastering the Art of Mental Reset Implementing the four-quarter system allows you to move through days with purpose, not just momentum. Each segment provides a mental reset when needed. For example, if your morning gets derailed by a crisis, you curb the frustration by refocusing on a fresh start in the late morning. The quarters provide checkpoints throughout the day to get back on course. This regular reset prevents compounding issues. An early mishap doesn’t ruin the whole day when you know another opportunity is coming in the next quarter. The Four Quarters in Action: A Case Study To illustrate how this plays out, let’s walk through a case study: Georgia adopts the four-quarter method to improve her time management skills. Here is how a sample day unfolds: Morning quarter (6am - 9am): Georgia wakes up early to go to the gym and drink a green smoothie. She then journals to get centered and reviews her priorities for the day. After meditating, she responds to urgent emails but saves substantive work for later. Late morning (9:30am - 1pm): Georgia blocks off this segment for making progress on a big presentation that’s due next week. She finds a focus zone in a cafe and spends the late morning working diligently on the deck. Afternoon (2pm - 5:30pm): When Georgia returns to the office, she feels drained. Rather than forcing more slide work, she uses the afternoon to meet with colleagues to gather feedback and refine the presentation. Regrouping helps breathe new life into the project. Evening (6pm - 9:30pm): Georgia heads home to enjoy dinner with her roommates, then calls her mom to catch up. She ends the day by reading fiction, stretching, and getting to bed early. By dividing her day into quarters, Georgia stays nimble when plans go awry. She doesn’t waste mental energy perseverating on setbacks. Each quarter offers a fresh start. Common Pitfalls to Avoid To avoid potential downsides of the four-quarter approach, keep these tips in mind: Review your typical daily schedule and determine natural breaking points to divide into quarters. Adjust if needed. Start on the weekend or a less busy day to begin practicing the quarter system without overhauling your routine. Don't let quarters 1 & 2 become “superior” to 3 & 4. Each plays an important role. Batch similar tasks within their relevant quarters, i.e. errands and admin in quarter 3. Set 1-3 priority goals or outcomes to accomplish within each quarter. Build in buffers between quarters to recharge. Don't schedule them back-to-back. Reflect at the end of each quarter. Continuously improve your time allocation. The four quarters provide a framework - not a formula. Adapt them as needed to your context while preserving time for spontaneity. The Power of Compartmentalization At its core, the four-quarter system leverages the psychology of compartmentalization. By separating experiences into categories, we limit contamination between them. For example, an argument with your partner doesn’t have to ruin your whole workday. By mentally filing it into the “personal” box, you reduce spillover into the “professional” domain. Compartmentalization allows us to isolate negatives into limited spaces, preventing spread. The four quarters effectively compartmentalize your day into discrete segments. This mental separation makes it easier to dissolve frustration at the end of difficult quarters, ensuring it doesn’t leak into the others. Each quarter gets a fresh start. The Brain Science of Mental Reset The effectiveness of quarterly mental reset is supported by research on the brain’s plasticity and renewal. Studies show activities like meditation, music, and time in nature can trigger restored cognitive performance by activating the default mode network in the brain associated with wakeful rest. In the quarter system, the gaps between segments allow this restoration to occur multiple times daily. You hit the mental reset button continually. This drives greater resilience by preventing negative emotional states from becoming entrenched. Each quarter interrupts rumination and stagnant thinking patterns. The brain breaks free from narrow grooves as you consciously shift between different modes of thinking across the quarters. The variation avoids the trap of remaining fixated on problems. By combining focused effort with intervals of recovery, the four quarters leverage neuroplasticity to enhance adaptability. Your brain stays agile and energized across the full day. Putting the Quarters Into Practice The real test comes in implementing the four-quarter method for yourself. Try applying it in your own life for one week and observe what happens. What insights emerged? Did certain quarters feel more productive than others? What was the greatest challenge? Use what you learn from your trial run to continue refining your quarter system. Tailor it to your energy rhythms and personal priorities. While rigid scheduling can backfire, having a guiding mental framework lends structure without rigidity. The quarters strike that delicate balance of providing direction while allowing flexibility. At its core, the four-quarter method is about building intentionality and mindfulness into your days. The goal is not to cram more into each segment but rather to bring your full presence and creativity to whatever the moment holds. Your days will unfold however they do. But having the quarter structure as an anchor allows you to flow through them with purpose, not just momentum. Each quarter is a chance to start again. Remember: Brute forcing endless grinding will lead to mediocrity and burnout - true success comes from strategically leveraging your natural mental rhythms, not pretending human limitations don't exist. If you enjoyed this article, I’d love to hear from you. Also published here. Write me at or at me and I’ll do my best to get back to everyone! scott@scottdclary.com tweet @ScottDClary

Building Mental Resilience: An Exploration of the Four Quarter Method

মানসিক স্থিতিস্থাপকতা তৈরি করা: ফোর কোয়ার্টার পদ্ধতির একটি অন্বেষণ

Aufbau geistiger Belastbarkeit: Eine Erkundung der Vier-Viertel-Methode

Desarrollar la resiliencia mental: una exploración del método de los cuatro trimestres

Renforcer la résilience mentale : une exploration de la méthode des quatre quarts

मानसिक लचीलेपन का निर्माण: चार चौथाई विधि का अन्वेषण

精神的な回復力の構築: フォー クォーター メソッドの探求

정신적 회복력 키우기: 4분기 방법 탐구

Construindo Resiliência Mental: Uma Exploração do Método dos Quatro Quartos

Повышение умственной устойчивости: исследование метода четырех четвертей

Zihinsel Dayanıklılığı Geliştirmek: Dört Çeyrek Yönteminin İncelenmesi

Xây dựng khả năng phục hồi tinh thần: Khám phá phương pháp Tứ quý

建立心理弹性：四季度法的探索

The Markup won multiple awards in the Society for News Design’s Best of News Design Creative Competition, which honors excellence in visual storytelling, design, and journalism produced in 2022. Still Loading The Markup’s investigation “Dollars to Megabits, You May Be Paying 400 Times As Much As Your Neighbor for Internet Service” has won a bronze medal in the investigative infographics category. Judges said: “The overall design compliments the power of impactful storytelling! A beautiful blend of facts and emotions is seamlessly woven together. Kudos to this small newsroom for delivering an exceptional visual narrative that truly captivates.” The investigation is the culmination of an eight-month effort that began when Markup reporters Leon Yin and Aaron Sankin set out to see what speeds and prices internet service providers were offering to households across the country. In the process, their work transformed into something even more urgent: an exposé revealing how a quartet of telecom giants had neglected to upgrade their networks with high-speed infrastructure in socioeconomically disadvantaged and less-White neighborhoods. Markup visualizations engineer Joel Eastwood created a series of graphics that appeared throughout the investigation and methodology, showing readers how starkly households in poorer, more diverse, and historically redlined neighborhoods receive worse internet for the same price. The team crafted a visual introduction to the investigation that highlighted one example in Kansas City, Mo., where two houses – located less than half a mile apart – get drastically different internet speeds but are asked to pay the same for their service. Congratulations to the team for recognition of their hard work: Joel Eastwood, Gabriel Hongsdusit, Aaron Sankin, Leon Yin, Jeremy Singer-Vine, Maria Puertas, Jill Jaroff, Evelyn Larrubia, and Paroma Soni. Impact Tracker and SmolLinks The Markup’s impact tracker and privacy-first link shortener, Smol Links, won an award of excellence in the internal storytelling tools for journalists category. In addition to writing about how we track impact, The Markup released an Airtable template of the structure of our impact database, so that other newsrooms and organizations can readily adopt or build upon our impact-tracking process. Smol Links, our WordPress plugin built on top of Shlink, lets editors generate short URLs inside their WordPress content management systems without compromising user privacy. Congratulations to Rachel Auslander and Dan Phiffer for recognition of their hard work. Congratulations, too, to all of this year’s SND honorees. Credits: The Markup Also published here Photo by John Jennings on Unsplash The Markup won multiple awards in the Society for News Design’s Best of News Design Creative Competition , which honors excellence in visual storytelling, design, and journalism produced in 2022. Society for News Design’s Best of News Design Creative Competition Still Loading The Markup’s investigation “ Dollars to Megabits, You May Be Paying 400 Times As Much As Your Neighbor for Internet Service ” has won a bronze medal in the investigative infographics category. Dollars to Megabits, You May Be Paying 400 Times As Much As Your Neighbor for Internet Service Judges said: “The overall design compliments the power of impactful storytelling! A beautiful blend of facts and emotions is seamlessly woven together. Kudos to this small newsroom for delivering an exceptional visual narrative that truly captivates.” The investigation is the culmination of an eight-month effort that began when Markup reporters Leon Yin and Aaron Sankin set out to see what speeds and prices internet service providers were offering to households across the country. In the process, their work transformed into something even more urgent: an exposé revealing how a quartet of telecom giants had neglected to upgrade their networks with high-speed infrastructure in socioeconomically disadvantaged and less-White neighborhoods. Markup visualizations engineer Joel Eastwood created a series of graphics that appeared throughout the investigation and methodology, showing readers how starkly households in poorer, more diverse, and historically redlined neighborhoods receive worse internet for the same price. The team crafted a visual introduction to the investigation that highlighted one example in Kansas City, Mo., where two houses – located less than half a mile apart – get drastically different internet speeds but are asked to pay the same for their service. Congratulations to the team for recognition of their hard work: Joel Eastwood, Gabriel Hongsdusit, Aaron Sankin, Leon Yin, Jeremy Singer-Vine, Maria Puertas, Jill Jaroff, Evelyn Larrubia, and Paroma Soni. Impact Tracker and SmolLinks The Markup’s impact tracker and privacy-first link shortener, Smol Links , won an award of excellence in the internal storytelling tools for journalists category. impact tracker Smol Links In addition to writing about how we track impact, The Markup released an Airtable template of the structure of our impact database, so that other newsrooms and organizations can readily adopt or build upon our impact-tracking process. Smol Links, our WordPress plugin built on top of Shlink , lets editors generate short URLs inside their WordPress content management systems without compromising user privacy. Airtable template our WordPress plugin Shlink Congratulations to Rachel Auslander and Dan Phiffer for recognition of their hard work. Congratulations, too, to all of this year’s SND honorees . this year’s SND honorees Credits: The Markup Credits: The Markup Also published here Also published here here Photo by John Jennings on Unsplash Photo by John Jennings on Unsplash John Jennings Unsplash

The Markup: Winner of Two SND Best of News Design Awards

Growing geopolitical unrest can be catastrophic on a humanitarian scale while leading to significant levels of volatility across international markets. For opportunistic investors, however, geopolitical uncertainty can pave the way for fresh opportunities to pick up defense stocks. Although the topic of buying defense stocks has historically proven to be a difficult one for retail investors to come to terms with on a moral basis, the industry is forecasted to grow to a market size of $367.3 billion by 2029 and will be buoyed by further government spending should the uncertain climate remain. Using the iShares US Aerospace & Defence ETF, an industry-specific exchange-traded fund with $6.3 billion AUM, we can see that since the flare-up of conflict in Ukraine in February 2022, the fund has grown around 40%. The ETF’s upward trend was sustained by an escalation of violence in the Middle East in late 2023, and the implications of these heightened aggressions are clear on Wall Street. “Increased geopolitical tensions usually force governments to allocate more money to the defense budget,” explains Maxim Manturov, head of investment advice at Freedom24. “Companies that supply essential defense equipment, technology, and services will benefit from this increased spending.” “Look for defense companies with a diversified portfolio across different defense sectors such as aerospace, cybersecurity, and military equipment manufacturing. Diversification helps reduce risks associated with fluctuations in specific defense segments and provides stability to the overall investment,” Manturov added. Despite its sustained growth in recent years against the backdrop of geopolitical uncertainty, investors may still need to be wary of an overvaluation creeping in throughout the sector. According to a recent Goldman Sachs warning, European defense stocks had been trading at a 45% premium to broader equity markets, placing stocks at risk of a market correction. The value of the industry is likely to be determined more by its growth in light of the changing geopolitical landscape. Should escalations in Europe and the Middle East begin to slow, it’s inconceivable that the sector can sustain its strong rate of growth. With this in mind, investors should look to conduct sufficient research when diversifying their portfolio with defense stocks and choose only companies with a sustainable commitment to innovation to buy. Let’s explore three defense stocks that could offer the best upside for investors moving forward: 1. Lockheed Martin (LMT) With a market capitalization of $110 billion, it’s impossible to ignore Lockheed Martin (NYSE:LMT) when looking at picking up defense stocks. Despite supply chain concerns impacting profits in 2024, Lockheed Martin beat sales predictions by 7.5% and earnings estimates by 10% in its first-quarter report and managed to earn $6.39 per share on sales of $17.2 billion for the period. Lockheed Martin is one of the world’s largest defense contractors and has built a reputation for its commitment to delivering a diverse portfolio of aerospace and defense solutions that feature aircraft and cybersecurity solutions among its wide repertoire. The firm is also renowned for its famous ‘Skunk Works’ research center in California, and Lockheed Martin continuously benefits from this industry-leading R&D to build next-generation electronics that can be seen across its suite of aerospace and defense products. 2. Raytheon Technologies (RTX) Another stock that’s proved its worth in 2024 is Raytheon Technologies (NYSE:RTX). Posting Wall Street growth of more than 15% over the first half of the year, Raytheon Technologies has rallied higher in recent weeks off the back of a positive first-quarter earnings report that saw earnings per share climb to $1.34, beating analyst expectations of $1.23. The company’s revenue of $19.31 billion also surpassed its $18.41 billion expectations by 4.86%. While Raytheon Technologies doesn’t have the same level of diversification as Lockheed Martin without the production of vehicles like warships and fighter jets, the firm is a key player throughout a wide range of global military platforms led by other contractors. Crucially, if investors were to have added the stock at the time of the landmark merger between Raytheon and United Technologies on April 3 2020, their ROI today would be in excess of 110%. This makes the stock one of the best performers on this list, and certainly a firm to keep a close eye on. 3. Northrop Grumman (NOC) One of the strongest five-year performers in this list is Northrop Grumman (NYSE:NOC). Despite widespread difficulty throughout Wall Street in 2022, NOC recorded growth of more than 36% throughout the calendar year. Although the stock has failed to build on its momentum entering 2024, posting a slight decline over the first two quarters of the year, Northrop Grumman managed to comfortably beat earnings expectations for the first quarter, posting earnings of $6.10 per share, beating the Zacks Consensus Estimate of $5.95 per share. Perhaps most impressively of all, is that this means NOC has managed to beat the consensus EPS estimates four times over the past four quarters, making it a stock that’s consistently confounding analysts. Another firm that specializes in aerospace and defense technology, including unmanned systems and cybersecurity innovation, Northrop Grumman is a key player in the growing stealth warfare industry and has developed a large space-focused portfolio. \ Given its long-term commitment to innovation, the firm appears to be a solid long-term bet for more sustained growth in the future. Opportunities for Research-Focused Investors The long-term growth of these defense stocks will rely heavily on the wider geopolitical landscape and the necessity of government spending on increasing defense-focused budgets. With this in mind, investors seeking to diversify their portfolios and add defense stocks should be mindful to conduct sufficient research into the state of defense spending and look to geopolitics for indicators as to whether industry-focused stocks are likely to show continued outperformance in the future. As uncertainty continues to afflict the geopolitical landscape, investors can still find opportunities within the growth of the defense sector with plenty of potential upside for its most innovative stocks. Growing geopolitical unrest can be catastrophic on a humanitarian scale while leading to significant levels of volatility across international markets. For opportunistic investors, however, geopolitical uncertainty can pave the way for fresh opportunities to pick up defense stocks. Although the topic of buying defense stocks has historically proven to be a difficult one for retail investors to come to terms with on a moral basis, the industry is forecasted to grow to a market size of $367.3 billion by 2029 and will be buoyed by further government spending should the uncertain climate remain. difficult one for retail investors difficult one for retail investors $367.3 billion by 2029 $367.3 billion by 2029 Using the iShares US Aerospace & Defence ETF, an industry-specific exchange-traded fund with $6.3 billion AUM, we can see that since the flare-up of conflict in Ukraine in February 2022, the fund has grown around 40%. The ETF’s upward trend was sustained by an escalation of violence in the Middle East in late 2023, and the implications of these heightened aggressions are clear on Wall Street. “Increased geopolitical tensions usually force governments to allocate more money to the defense budget,” explains Maxim Manturov, head of investment advice at Freedom24 . “Companies that supply essential defense equipment, technology, and services will benefit from this increased spending.” Freedom24 Freedom24 “Look for defense companies with a diversified portfolio across different defense sectors such as aerospace, cybersecurity, and military equipment manufacturing. Diversification helps reduce risks associated with fluctuations in specific defense segments and provides stability to the overall investment,” Manturov added. Despite its sustained growth in recent years against the backdrop of geopolitical uncertainty, investors may still need to be wary of an overvaluation creeping in throughout the sector. According to a recent Goldman Sachs warning , European defense stocks had been trading at a 45% premium to broader equity markets, placing stocks at risk of a market correction. recent Goldman Sachs warning recent Goldman Sachs warning The value of the industry is likely to be determined more by its growth in light of the changing geopolitical landscape. Should escalations in Europe and the Middle East begin to slow, it’s inconceivable that the sector can sustain its strong rate of growth. With this in mind, investors should look to conduct sufficient research when diversifying their portfolio with defense stocks and choose only companies with a sustainable commitment to innovation to buy. Let’s explore three defense stocks that could offer the best upside for investors moving forward: 1. Lockheed Martin (LMT) With a market capitalization of $110 billion, it’s impossible to ignore Lockheed Martin (NYSE:LMT) when looking at picking up defense stocks. Despite supply chain concerns impacting profits in 2024, Lockheed Martin beat sales predictions by 7.5% and earnings estimates by 10% in its first-quarter report and managed to earn $6.39 per share on sales of $17.2 billion for the period. beat sales predictions by 7.5% beat sales predictions by 7.5% Lockheed Martin is one of the world’s largest defense contractors and has built a reputation for its commitment to delivering a diverse portfolio of aerospace and defense solutions that feature aircraft and cybersecurity solutions among its wide repertoire. cybersecurity The firm is also renowned for its famous ‘Skunk Works’ research center in California, and Lockheed Martin continuously benefits from this industry-leading R&D to build next-generation electronics that can be seen across its suite of aerospace and defense products. 2. Raytheon Technologies (RTX) Another stock that’s proved its worth in 2024 is Raytheon Technologies (NYSE:RTX). Posting Wall Street growth of more than 15% over the first half of the year, Raytheon Technologies has rallied higher in recent weeks off the back of a positive first-quarter earnings report that saw earnings per share climb to $1.34 , beating analyst expectations of $1.23. earnings per share climb to $1.34 earnings per share climb to $1.34 The company’s revenue of $19.31 billion also surpassed its $18.41 billion expectations by 4.86%. While Raytheon Technologies doesn’t have the same level of diversification as Lockheed Martin without the production of vehicles like warships and fighter jets, the firm is a key player throughout a wide range of global military platforms led by other contractors. Crucially, if investors were to have added the stock at the time of the landmark merger between Raytheon and United Technologies on April 3 2020, their ROI today would be in excess of 110%. This makes the stock one of the best performers on this list, and certainly a firm to keep a close eye on. 3. Northrop Grumman (NOC) One of the strongest five-year performers in this list is Northrop Grumman (NYSE:NOC). Despite widespread difficulty throughout Wall Street in 2022, NOC recorded growth of more than 36% throughout the calendar year. Although the stock has failed to build on its momentum entering 2024, posting a slight decline over the first two quarters of the year, Northrop Grumman managed to comfortably beat earnings expectations for the first quarter, posting earnings of $6.10 per share , beating the Zacks Consensus Estimate of $5.95 per share. posting earnings of $6.10 per share posting earnings of $6.10 per share Perhaps most impressively of all, is that this means NOC has managed to beat the consensus EPS estimates four times over the past four quarters, making it a stock that’s consistently confounding analysts. Another firm that specializes in aerospace and defense technology, including unmanned systems and cybersecurity innovation, Northrop Grumman is a key player in the growing stealth warfare industry and has developed a large space-focused portfolio. \ cybersecurity Given its long-term commitment to innovation, the firm appears to be a solid long-term bet for more sustained growth in the future. Opportunities for Research-Focused Investors The long-term growth of these defense stocks will rely heavily on the wider geopolitical landscape and the necessity of government spending on increasing defense-focused budgets. With this in mind, investors seeking to diversify their portfolios and add defense stocks should be mindful to conduct sufficient research into the state of defense spending and look to geopolitics for indicators as to whether industry-focused stocks are likely to show continued outperformance in the future. As uncertainty continues to afflict the geopolitical landscape, investors can still find opportunities within the growth of the defense sector with plenty of potential upside for its most innovative stocks.

Capitalizing on Uncertainty: 3 Defense Stocks To Look Out For as Geopolitical Tensions Rise

Asia is one of the biggest crypto markets on the earth, among the European Union and the USA. Hong Kong became the first jurisdiction on this continent to approve Bitcoin and Ethereum ETF. Regulators can give investors access to Bitcoin and Ethereum ETFs in two ways. The first option is to allow qualified investors to invest in foreign Bitcoin ETFs, which Thailand already did in March of this year. And recently, Thailand approved a local Bitcoin ETF in May. Let's talk about a few more countries in the region where ETF approval may affect the overall crypto market capitalization. It looks like the most prospective countries for crypto ETF approval now are South Korea, Taiwan, and maybe Japan and Singapore. South Korea This country has a wide level of crypto adoption. Just one fact to mention - in the first quarter of this year, the Korean Won was leading in crypto to fiat trading volume on centralized exchanges ($456 bln), it has even surpassed the US dollar ($445 bln). Despite that, this January, the local regulator rejected local Bitcoin ETFs. But the situation changed in April - then, the Democratic party declared ETF approval as a part of its сampaign - and this party won elections in the local parliament. However, crypto ETF approval in South Korea at least needs some legislative actions based on local experts. Also, there is a sign that Korea continues to tighten crypto regulations - local regulator FSC is tightening its approach to the NFT. With this in mind, it is still not very clear how the ruling party will fulfill its promises if the regulator has a different stance. At least don't count on the rapid adoption of Bitcoin ETFs in Korea, although it is quite possible in the medium term In July, the Korean Act on the Protection of Virtual Asset Users will come into power. The new ruling party has just gained control of the parliament, so we'll have to wait to see how their approach to crypto regulation changes after the election. Japan The adoption of Bitcoin ETFs is a gradual process - one indication that it is likely is the regulator's loyalty to the purchase of Bitcoin by large publicly traded companies. This is what Microstrategy did in the US, and now Metaplanet is doing it in Japan - it has publicly disclosed its Bitcoin savings. The stock price jumped 127% for 1 week in May. Metaplanet's rise is a sure sign of demand in Japan for a Bitcoin ETF counterpart before it officially exists. The approval of Japan's own bitcoin ETFs is still on the table. As in Thailand, Japanese stock exchanges need regulatory approval to make U.S. Bitcoin ETFs available. And there's no information yet to make it happen. But the growing demand and high elaboration of crypto regulation in the country, and competition with other Asian exchanges, primarily with Hong Kong, may push the process this year. Taiwan Approval of crypto ETFs as investing products in Taiwan is likely to happen in 2024, but still stays uncertain. Currently, this jurisdiction still has some gaps in its crypto regulations for VASPs and AML. New rules for crypto probably will be presented only in September 2024. Any decisions on crypto spot ETFs for the local market will probably be delayed up to this date. However, currently in Taiwan, it’s already possible to buy US crypto ETFs for residents of Taiwan using local brokers. But there has yet to be a resolution from the local regulator on this. Last but not least, the Taiwanese ETF market is now booming with more than $123 bln in capitalization. This means that regulators have a relatively innovative approach to crypto regulations and can approve crypto ETFs, too. Singapore The approval of Bitcoin ETFs in Singapore depends on the local finance regulator, the Monetary Authority of Singapore (MAS). This March, the regulator published a notice stating that it doesn't want to give retail investors access to crypto ETFs. Local brokers follow this approach, either giving access to US spot Bitcoin ETFs for retail investors with special notice or not giving it at all. At the same time, Singapore has a significant demand for Bitcoin ETFs. Based on an Independent Reserve survey this March, about one-third of locals would like to invest in Spot Bitcoin ETF. So Singapore likely won't be the next jurisdiction according to the position of the local authorities, but again, the high activity of crypto asset lobbyists and the level of adopting institutional investors is forcing regulators to accelerate. UAE The United Arab Emirates has a good crypto regulation framework and may be classified as a crypto offshore of Asia. At the same time, there is no evidence that this jurisdiction plans to introduce Bitcoin ETFs this year. But there is a high demand for it - according to Bitget research, 72% of local users are interested in cryptocurrencies. So currently in Dubai, there are available crypto futures derivatives, but not ETFs. Also, local regulators need a clear policy about Bitcoin ETFs. Next Wave: India and Other Large Economies? India and Indonesia are the only countries in the world, which imposed tax on crypto transactions and basically have an unfavorable approach to crypto regulation. At the same time, these countries have a relatively high level of stock market capitalization. Moreover, in India, local investors have a right to invest outside a country up to $250,000 per year and purchase securities. They already can invest in foreign crypto ETFs by this rule. So probably after leading in fintech innovation countries like South Korea, Japan, Singapore, and others approve Bitcoin ETF, India may join. Also, there's a chance that mainland China will get access to crypto ETFs at some point. Roughly, the same can be said about the positions of regulators in the Philippines and most Arab Asian countries - they will see how ETFs trade in other jurisdictions in the region and only then join. Probably these jurisdictions won't approve any crypto ETFs in 2024. Conclusion With the growing demand for crypto in most of the major economies in Asia, we can likely wait for crypto ETF approval in South Korea and 2-3 more Asian jurisdictions this year. The main reason why this can happen - the presence of ETFs in other countries such as the US and Hong Kong. The absence of Bitcoin ETFs on local exchanges will provoke an outflow of crypto investors' funds to jurisdictions where such investment instruments are legal Asia is one of the biggest crypto markets on the earth, among the European Union and the USA. Hong Kong became the first jurisdiction on this continent to approve Bitcoin and Ethereum ETF. Regulators can give investors access to Bitcoin and Ethereum ETFs in two ways. The first option is to allow qualified investors to invest in foreign Bitcoin ETFs, which Thailand already did in March of this year. And recently, Thailand approved a local Bitcoin ETF in May. Let's talk about a few more countries in the region where ETF approval may affect the overall crypto market capitalization. It looks like the most prospective countries for crypto ETF approval now are South Korea, Taiwan, and maybe Japan and Singapore. South Korea This country has a wide level of crypto adoption. Just one fact to mention - in the first quarter of this year, the Korean Won was leading in crypto to fiat trading volume on centralized exchanges ($456 bln), it has even surpassed the US dollar ($445 bln). Despite that, this January, the local regulator rejected local Bitcoin ETFs. But the situation changed in April - then, the Democratic party declared ETF approval as a part of its сampaign - and this party won elections in the local parliament. However, crypto ETF approval in South Korea at least needs some legislative actions based on local experts. rejected Also, there is a sign that Korea continues to tighten crypto regulations - local regulator FSC is tightening its approach to the NFT. With this in mind, it is still not very clear how the ruling party will fulfill its promises if the regulator has a different stance. At least don't count on the rapid adoption of Bitcoin ETFs in Korea, although it is quite possible in the medium term In July, the Korean Act on the Protection of Virtual Asset Users will come into power. The new ruling party has just gained control of the parliament, so we'll have to wait to see how their approach to crypto regulation changes after the election. Japan The adoption of Bitcoin ETFs is a gradual process - one indication that it is likely is the regulator's loyalty to the purchase of Bitcoin by large publicly traded companies. This is what Microstrategy did in the US, and now Metaplanet is doing it in Japan - it has publicly disclosed its Bitcoin savings. The stock price jumped 127% for 1 week in May. Metaplanet's rise is a sure sign of demand in Japan for a Bitcoin ETF counterpart before it officially exists. The approval of Japan's own bitcoin ETFs is still on the table. As in Thailand, Japanese stock exchanges need regulatory approval to make U.S. Bitcoin ETFs available. And there's no information yet to make it happen. But the growing demand and high elaboration of crypto regulation in the country, and competition with other Asian exchanges, primarily with Hong Kong, may push the process this year. Taiwan Approval of crypto ETFs as investing products in Taiwan is likely to happen in 2024, but still stays uncertain. Currently, this jurisdiction still has some gaps in its crypto regulations for VASPs and AML. New rules for crypto probably will be presented only in September 2024. Any decisions on crypto spot ETFs for the local market will probably be delayed up to this date. However, currently in Taiwan, it’s already possible to buy US crypto ETFs for residents of Taiwan using local brokers. But there has yet to be a resolution from the local regulator on this. Last but not least, the Taiwanese ETF market is now booming with more than $123 bln in capitalization. This means that regulators have a relatively innovative approach to crypto regulations and can approve crypto ETFs, too. Singapore The approval of Bitcoin ETFs in Singapore depends on the local finance regulator, the Monetary Authority of Singapore (MAS). This March, the regulator published a notice stating that it doesn't want to give retail investors access to crypto ETFs. Local brokers follow this approach, either giving access to US spot Bitcoin ETFs for retail investors with special notice or not giving it at all. At the same time, Singapore has a significant demand for Bitcoin ETFs. Based on an Independent Reserve survey this March, about one-third of locals would like to invest in Spot Bitcoin ETF. So Singapore likely won't be the next jurisdiction according to the position of the local authorities, but again, the high activity of crypto asset lobbyists and the level of adopting institutional investors is forcing regulators to accelerate. UAE UAE The United Arab Emirates has a good crypto regulation framework and may be classified as a crypto offshore of Asia. At the same time, there is no evidence that this jurisdiction plans to introduce Bitcoin ETFs this year. But there is a high demand for it - according to Bitget research, 72% of local users are interested in cryptocurrencies. So currently in Dubai, there are available crypto futures derivatives, but not ETFs. Also, local regulators need a clear policy about Bitcoin ETFs. Next Wave: India and Other Large Economies? India and Indonesia are the only countries in the world, which imposed tax on crypto transactions and basically have an unfavorable approach to crypto regulation. At the same time, these countries have a relatively high level of stock market capitalization. Moreover, in India, local investors have a right to invest outside a country up to $250,000 per year and purchase securities. They already can invest in foreign crypto ETFs by this rule. So probably after leading in fintech innovation countries like South Korea, Japan, Singapore, and others approve Bitcoin ETF, India may join. Also, there's a chance that mainland China will get access to crypto ETFs at some point. Roughly, the same can be said about the positions of regulators in the Philippines and most Arab Asian countries - they will see how ETFs trade in other jurisdictions in the region and only then join. Probably these jurisdictions won't approve any crypto ETFs in 2024. Conclusion Conclusion With the growing demand for crypto in most of the major economies in Asia, we can likely wait for crypto ETF approval in South Korea and 2-3 more Asian jurisdictions this year. The main reason why this can happen - the presence of ETFs in other countries such as the US and Hong Kong. The absence of Bitcoin ETFs on local exchanges will provoke an outflow of crypto investors' funds to jurisdictions where such investment instruments are legal

Hong Kong and Thailand Approved Crypto ETFs: Who Is Next in Asia?

Author:
(1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu);
(2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu);
(3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Table of Links Abstract Introduction & Related Work Method Analysis & Results Discussion & Conclusion References REFERENCES [1] M. Deutsch, The resolution of conflict: Constructive and destructive processes. Yale University Press, 1973. [2] J. Riegelsberger, M. A. Sasse, and J. D. McCarthy, “The researcher’s dilemma: Evaluating trust in computer-mediated communication,” International Journal of Human Computer Studies, vol. 58, no. 6, pp. 759–781, 2003. [3] J. K. Rempel, J. G. Holmes, and M. P. Zanna, “Trust in close relationships,” Journal of Personality and Social Psychology, vol. 49, no. 1, pp. 95–112, 1985. [4] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004. [5] D. Ullman and B. F. Malle, “Measuring gains and losses in humanrobot trust: Evidence for differentiable components of trust,” in Proceedings of the 14th ACM/IEEE International Conference on HumanRobot Interaction, ser. HRI ’19. IEEE Press, 2019, pp. 618–619. [6] R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” Academy of management review, vol. 20, no. 3, pp. 709–734, 1995. [7] W. Kim, N. Kim, J. B. Lyons, and C. S. Nam, “Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach,” Applied Ergonomics, vol. 85, may 2020. [8] C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (RoSAS) development and validation,” in ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: IEEE Computer Society, 2017, pp. 254–262. [9] S. T. Fiske, A. J. Cuddy, and P. Glick, “Universal dimensions of social cognition: Warmth and competence,” Trends in Cognitive Sciences, vol. 11, no. 2, pp. 77–83, 2007. [10] A. J. Cuddy, S. T. Fiske, and P. Glick, “The BIAS Map: Behaviors From Intergroup Affect and Stereotypes,” Journal of Personality and Social Psychology, vol. 92, no. 4, pp. 631–648, 2007. [11] B. Reeves, J. Hancock, and X. Liu, “Social robots are like real people: First impressions, attributes, and stereotyping of social robots,” Technology, Mind, and Behavior, vol. 1, no. 1, 2020. [12] C. Nam and J. Lyons, Eds., Trust in human-robot interaction: Research and applications. Elsevier, 2020. [13] P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. Chen, E. J. De Visser, and R. Parasuraman, “A meta-analysis of factors affecting trust in human-robot interaction,” Human Factors, vol. 53, no. 5, pp. 517– 527, 2011. [14] S. Naneva, M. Sarda Gou, T. L. Webb, and T. J. Prescott, “A systematic review of attitudes, anxiety, acceptance, and trust towards social robots,” International Journal of Social Robotics, 2020. [15] M. Salem and K. Dautenhahn, “Evaluating trust and safety in HRI: Practical issues and ethical challenges,” Emerging Policy and Ethics of Human-Robot Interaction, 2015. [16] M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, “Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on HumanRobot Interaction. ACM, 2015, pp. 141–148. [17] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly: Management Information Systems, vol. 13, no. 3, pp. 319–339, 1989. [18] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly: Management Information Systems, vol. 27, no. 3, pp. 425– 478, 2003. [19] M. J. Mataric and B. Scassellati, “Socially assistive robotics,” in ´ Springer Handbook of Robotics, K. O. Siciliano B., Ed. Springer, 2016, pp. 1973–1994. [20] M. S. Fritz and D. P. MacKinnon, “Required sample size to detect the mediated effect,” Psychological Science, vol. 18, no. 3, pp. 233–239, 2007. [21] A. J. Fairchild, D. P. MacKinnon, M. P. Taborga, and A. B. Taylor, “R2 effect-size measures for mediation analysis,” Behavior research methods, vol. 41, no. 2, pp. 486–498, 2009. [22] D. L. Streiner, “Finding our way: An introduction to path analysis,” Canadian Journal of Psychiatry, vol. 50, no. 2, pp. 115–122, 2005. [23] A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd ed. The Guilford Press, 2022. This paper is available on arxiv under CC 4.0 license. Author: (1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu); (2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu); (3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Author: Author: (1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu); (2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu); (3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu). Table of Links Abstract Introduction & Related Work Abstract Introduction & Related Work Method Method Analysis & Results Analysis & Results Discussion & Conclusion Discussion & Conclusion References References REFERENCES [1] M. Deutsch, The resolution of conflict: Constructive and destructive processes. Yale University Press, 1973. [2] J. Riegelsberger, M. A. Sasse, and J. D. McCarthy, “The researcher’s dilemma: Evaluating trust in computer-mediated communication,” International Journal of Human Computer Studies, vol. 58, no. 6, pp. 759–781, 2003. [3] J. K. Rempel, J. G. Holmes, and M. P. Zanna, “Trust in close relationships,” Journal of Personality and Social Psychology, vol. 49, no. 1, pp. 95–112, 1985. [4] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004. [5] D. Ullman and B. F. Malle, “Measuring gains and losses in humanrobot trust: Evidence for differentiable components of trust,” in Proceedings of the 14th ACM/IEEE International Conference on HumanRobot Interaction, ser. HRI ’19. IEEE Press, 2019, pp. 618–619. [6] R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” Academy of management review, vol. 20, no. 3, pp. 709–734, 1995. [7] W. Kim, N. Kim, J. B. Lyons, and C. S. Nam, “Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach,” Applied Ergonomics, vol. 85, may 2020. [8] C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (RoSAS) development and validation,” in ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: IEEE Computer Society, 2017, pp. 254–262. [9] S. T. Fiske, A. J. Cuddy, and P. Glick, “Universal dimensions of social cognition: Warmth and competence,” Trends in Cognitive Sciences, vol. 11, no. 2, pp. 77–83, 2007. [10] A. J. Cuddy, S. T. Fiske, and P. Glick, “The BIAS Map: Behaviors From Intergroup Affect and Stereotypes,” Journal of Personality and Social Psychology, vol. 92, no. 4, pp. 631–648, 2007. [11] B. Reeves, J. Hancock, and X. Liu, “Social robots are like real people: First impressions, attributes, and stereotyping of social robots,” Technology, Mind, and Behavior, vol. 1, no. 1, 2020. [12] C. Nam and J. Lyons, Eds., Trust in human-robot interaction: Research and applications. Elsevier, 2020. [13] P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. Chen, E. J. De Visser, and R. Parasuraman, “A meta-analysis of factors affecting trust in human-robot interaction,” Human Factors, vol. 53, no. 5, pp. 517– 527, 2011. [14] S. Naneva, M. Sarda Gou, T. L. Webb, and T. J. Prescott, “A systematic review of attitudes, anxiety, acceptance, and trust towards social robots,” International Journal of Social Robotics, 2020. [15] M. Salem and K. Dautenhahn, “Evaluating trust and safety in HRI: Practical issues and ethical challenges,” Emerging Policy and Ethics of Human-Robot Interaction, 2015. [16] M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, “Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on HumanRobot Interaction. ACM, 2015, pp. 141–148. [17] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly: Management Information Systems, vol. 13, no. 3, pp. 319–339, 1989. [18] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly: Management Information Systems, vol. 27, no. 3, pp. 425– 478, 2003. [19] M. J. Mataric and B. Scassellati, “Socially assistive robotics,” in ´ Springer Handbook of Robotics, K. O. Siciliano B., Ed. Springer, 2016, pp. 1973–1994. [20] M. S. Fritz and D. P. MacKinnon, “Required sample size to detect the mediated effect,” Psychological Science, vol. 18, no. 3, pp. 233–239, 2007. [21] A. J. Fairchild, D. P. MacKinnon, M. P. Taborga, and A. B. Taylor, “R2 effect-size measures for mediation analysis,” Behavior research methods, vol. 41, no. 2, pp. 486–498, 2009. [22] D. L. Streiner, “Finding our way: An introduction to path analysis,” Canadian Journal of Psychiatry, vol. 50, no. 2, pp. 115–122, 2005. [23] A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd ed. The Guilford Press, 2022. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Trust and Acceptance of Social Robots: References

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Results

Nvidia has become successful. So successful, in fact, that it does not care about alienating an entire demographic that was core to its success. This was underscored at the recent Computex event in Taiwan where the CEOs of three most prominent silicon companies (Nvidia's included) took to the stage to discuss new products and their future road map. And if you're a gamer, boy, was it depressing. Nvidia CEO Jensen Huang's two-hour long presentation was only artificial intelligence focused, to the point that it got boring and irked many who were hoping to learn more about the company's gaming-focused hardware. As TechRadar's John Loeffler put it, Huang dismissed the very same GeForce graphic cards that put the company on the map all those decades ago. This was corroborated by Hardware Unboxed's Steve Walton, who described the keynote address' overall vibe as one where Nvidia wanted to distance itself from the "silliness of gaming" and be taken seriously as a data-center type company instead. So why the shift? Nvidia's most recent quarterly results showed that the data center segment, which sells the hardware powering the world's generative AI tools, made roughly nine times more money than the gaming division ($22.6 billion vs. $2.6 billion). In fact, the data center segment contributed 86% towards the company's record revenue ($26.04 billion) this past quarter, compared to a measly 10% by the gaming segment. So Huang's just doubling down on what's working for the business. What's quite bothersome is that because Nvidia is the market leader,  Intel and AMD have no choice but to follow the trends set by Team Green — so of course, both companies also spent a considerable amount of time talking about their plans for profiting off of that sweet, sweet AI pie. But not without whetting the appetites of eager gamers with some product announcements. Despite holding next to no market share in the GPU market, Intel isn't giving up on the hardware that powers video games and AI workloads. After weeks of leaks and speculation, the company finally gave its first glimpse of the architecture that would power its next generation of graphic cards. Codenamed Battlemage, the upcoming graphic cards are designed to be more efficient than the earlier Alchemist variants, which, "if realized, [..] would rectify a major usability shortcoming of Alchemist," GamersNexus wrote in a blog post. For those that did not follow Intel's attempts at creating discrete GPUs, the Alchemist cards were plagued with driver issues that made them a hard sell for reviewers. The only reason the likes of Linus Tech Tips recommended the card was to encourage competition in the space. The Alchemist cards fare much better today than when they launched — thanks to driver-side improvements from Intel — but are still a hard sell. Which brings us to AMD. Team Red is the underdog in both the GPU and CPU markets, losing out to Nvidia on graphic cards, and Intel on central processors (though that seems to be changing). Competing on both fronts is obviously not easy (or cheap!), but the company has seen a massive turn around under the leadership of Lisa Su — going from holding 1.4% of the CPU market share in 2017 to a respectable 23.6% at the end of 2022. The company also holds 12% of the GPU market, which might not sound like a lot, but does offer a bit of competition to Nvidia which dominates the market. So, what is Team Red doing to be more of a contender in the market? For starters, the company announced its next line of processors at Computex. The AMD Ryzen 9000-series chips are supposedly going to be 15% faster than their predecessors, but we'll have to wait for the reviews to roll in to know for sure (15% doesn't really sound like a lot, if I'm being honest). Not a lot on GPUs though; the company's 7-series cards were pretty 'meh', and with Nvidia not actually offering any details on the 50-series cards, AMD probably didn't feel the need to share any details on what to expect next. Apart from these three companies, there was a lot of chatter about Qualcomm, the maker of Snapdragon chips that power mobile devices. Apparently, the company expects its processors to power roughly 50% of Windows PC devices within the next five years. That's a bold claim, but has a lot to do with a recent Microsoft announcement about a new class of Windows computers designed just for AI: Copilot+ PCs. Those PCs will be powered using Qualcomm's Snapdragon X processors, which is supposedly designed to handle AI workflows, and is thus better at it. Anyway, that was your Computex recap. Nvidia ranked #1 on HackerNoon's Tech Company Rankings this week. Sponsored Content Meet the device ushering in a new era of hearing clarity Discover unparalleled speech clarity with this advanced, state-of-the-art hearing aid. With crystal-clear sound delivered to your ears and a sleek, discreet design powered by German technology, you can now enjoy clear speech with minimum background distractions, even in the noisiest environments. Take comfort in our commitment to your hearing and try our 45-day no-risk trial today. Start your no-risk trial today In Other News.. 📰 Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk
Apple needs to focus on making AI useful, not flashy — via TechCrunch
Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN
Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters
Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios
Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here.* Nvidia has become successful. So successful, in fact, that it does not care about alienating an entire demographic that was core to its success. Nvidia Nvidia care care This was underscored at the recent Computex event in Taiwan where the CEOs of three most prominent silicon companies (Nvidia's included) took to the stage to discuss new products and their future road map. And if you're a gamer, boy, was it depressing. Nvidia CEO Jensen Huang's two-hour long presentation was only artificial intelligence focused, to the point that it got boring and irked many who were hoping to learn more about the company's gaming-focused hardware . two-hour long presentation two-hour long presentation only got boring got boring irked many irked many gaming-focused hardware gaming-focused hardware As TechRadar's John Loeffler put it , Huang dismissed the very same GeForce graphic cards that put the company on the map all those decades ago. This was corroborated by Hardware Unboxed's Steve Walton, who described the keynote address' overall vibe as one where Nvidia wanted to distance itself from the "silliness of gaming" and be taken seriously as a data-center type company instead. put it put it described described So why the shift? Nvidia's most recent quarterly results showed that the data center segment, which sells the hardware powering the world's generative AI tools, made roughly nine times more money than the gaming division ($22.6 billion vs. $2.6 billion). quarterly results quarterly results In fact, the data center segment contributed 86% towards the company's record revenue ($26.04 billion) this past quarter, compared to a measly 10% by the gaming segment. So Huang's just doubling down on what's working for the business. What's quite bothersome is that because Nvidia is the market leader, Intel and AMD have no choice but to follow the trends set by Team Green — so of course, both companies also spent a considerable amount of time talking about their plans for profiting off of that sweet, sweet AI pie. But not without whetting the appetites of eager gamers with some product announcements. Intel Intel AMD AMD Despite holding next to no market share in the GPU market, Intel isn't giving up on the hardware that powers video games and AI workloads. After weeks of leaks and speculation , the company finally gave its first glimpse of the architecture that would power its next generation of graphic cards. no market share no market share leaks leaks speculation speculation first glimpse first glimpse Codenamed Battlemage, the upcoming graphic cards are designed to be more efficient than the earlier Alchemist variants, which, "if realized, [..] would rectify a major usability shortcoming of Alchemist," GamersNexus wrote in a blog post. For those that did not follow Intel's attempts at creating discrete GPUs, the Alchemist cards were plagued with driver issues that made them a hard sell for reviewers. The only reason the likes of Linus Tech Tips recommended the card was to encourage competition in the space. recommended recommended The Alchemist cards fare much better today than when they launched — thanks to driver-side improvements from Intel — but are still a hard sell . driver-side improvements driver-side improvements hard sell hard sell Which brings us to AMD. Team Red is the underdog in both the GPU and CPU markets, losing out to Nvidia on graphic cards, and Intel on central processors (though that seems to be changing ). changing changing Competing on both fronts is obviously not easy (or cheap!), but the company has seen a massive turn around under the leadership of Lisa Su — going from holding 1.4% of the CPU market share in 2017 to a respectable 23.6% at the end of 2022. The company also holds 12% of the GPU market, which might not sound like a lot, but does offer a bit of competition to Nvidia which dominates the market. respectable respectable So, what is Team Red doing to be more of a contender in the market? For starters, the company announced its next line of processors at Computex. The AMD Ryzen 9000-series chips are supposedly going to be 15% faster than their predecessors, but we'll have to wait for the reviews to roll in to know for sure (15% doesn't really sound like a lot, if I'm being honest). 15% faster 15% faster Not a lot on GPUs though; the company's 7-series cards were pretty 'meh', and with Nvidia not actually offering any details on the 50-series cards, AMD probably didn't feel the need to share any details on what to expect next. Apart from these three companies, there was a lot of chatter about Qualcomm, the maker of Snapdragon chips that power mobile devices. Apparently, the company expects its processors to power roughly 50% of Windows PC devices within the next five years. lot of chatter lot of chatter roughly 50% roughly 50% That's a bold claim, but has a lot to do with a recent Microsoft announcement about a new class of Windows computers designed just for AI: Copilot+ PCs. Microsoft Microsoft announcement announcement just Those PCs will be powered using Qualcomm's Snapdragon X processors, which is supposedly designed to handle AI workflows, and is thus better at it. Anyway, that was your Computex recap. Nvidia ranked #1 on HackerNoon's Tech Company Rankings this week. Tech Company Rankings Tech Company Rankings Sponsored Content Sponsored Content Sponsored Content Meet the device ushering in a new era of hearing clarity Meet the device ushering in a new era of hearing clarity Discover unparalleled speech clarity with this advanced, state-of-the-art hearing aid. With crystal-clear sound delivered to your ears and a sleek, discreet design powered by German technology, you can now enjoy clear speech with minimum background distractions, even in the noisiest environments. Take comfort in our commitment to your hearing and try our 45-day no-risk trial today. Start your no-risk trial today Start your no-risk trial today In Other News.. 📰 In Other News.. 📰 Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk Apple needs to focus on making AI useful, not flashy — via TechCrunch Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk CoinDesk CoinDesk Apple needs to focus on making AI useful, not flashy — via TechCrunch TechCrunch TechCrunch Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN CNN CNN Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters Reuters Reuters Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios Axios Axios Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. * *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings here here

Yikes! Nvidia's CEO Doesn't Care About Gamers

Background Seeing the amazing response to the first 3 articles of this series, I had to come out with a 4th part. In the previous 3 articles, we have discussed performance metric definitions, instrumentation, and scalability for conversation AI agents. In case you have not checked out the previous articles, here are the links: Part 1 - Metrics: Swallow the Red Pill
Part 2 - Metrics Reloaded: The Oracle
Part 3 - The Metrics Revolution: Scaling In this article, we will be discussing how to make these metrics more actionable (using the latest LLM advances) in order to improve the performance on an ongoing basis. The aim will be to keep the discussion simplified and fairly high level for everyone working in this domain. The Problem User Perceived Metrics and User Reported Metrics are 2 high-level classes of metrics we have discussed. Traditionally, the former is thought of as a system-level metric - these metrics are measured directly from logs. As a result, User Perceived Metrics are actionable by nature and hence operational. Operational metrics are tracked on a regular basis from production logs and can be used for target setting w.r.t team wide OKRs. However, even though User Perceived Metrics are easy to operationalize, it should be noted that these are “perceived” and not “actual” user metrics. As a result, hill-climbing on these metrics might not lead to a significant improvement in user perception of your conversational AI agent. This might lead to in-efficient management of resources if these projects span across multiple quarters. There needs to be a way to measure the expected impact of all performance improvements directly w.r.t User Reported Metrics. This should be treated as the “north star” impact. So, what is the problem? Direct user feedback is expected to be unstructured which is not actionable and different to operationalize. Detailed user-reported feedback should be unstructured by nature. If the user-reported feedback is made structured, then it can end up focusing on areas the internal team is already aware of. In addition to these, User Reported Metrics are also affected by factors such as seasonality and company perception. The impact on User Perceived Metrics can be estimated more accurately but User Reported Metrics has a lot of uncontrollable factors. Solution The unstructured User Reported Feedback should be converted into a structured format that can be made actionable. There can be specific ML models trained for the purpose of converting unstructured feedback into existing system-level metrics. It should be noted that it might be more practical to use User Reported Metrics’ primary goal for “recent” user metric regressions to protect against the inherent skew in these metrics. For more horizontal long-term projects, these metrics should be used to measure the impact on user perception along with system-level metrics. LLMs Are the Game Changers Now the question remains, what is the effort needed to train ML models for the specific metrics we are looking for? With the recent increase in popularity and availability of LLMs, it might be possible to use out-of-the-box APIs to convert unstructured feedback into something that can be tracked and measured similar to system-level metrics. It is important to note that with the increase in the number of tokens that LLMs can process, a lot of product-specific information can be provided as part of the “prompt” itself. As a result, off-the-shelf LLM APIs along with some prompt engineering can provide actionable User Reported Metrics. This provides a really fast way to assess the impact of system-level metric improvement projects on user perception which can be useful in prioritizing performance improvement projects. Even with this approach of structured User Reported Metrics, there is still room for unexpected changes. However, it can be assumed with some level of confidence, that if a specific project (aimed at improving a system-level metric) ends up positively impacting Reported Metrics, then the project is most likely actually improving user perception. However, there is no guarantee that all actually “good” changes will always effectively improve User Reported Metrics. As a result, it is important to use a mix of both to prioritize and evaluate performance improvement projects. Background Seeing the amazing response to the first 3 articles of this series, I had to come out with a 4th part. In the previous 3 articles, we have discussed performance metric definitions, instrumentation, and scalability for conversation AI agents. In case you have not checked out the previous articles, here are the links: Part 1 - Metrics: Swallow the Red Pill Part 2 - Metrics Reloaded: The Oracle Part 3 - The Metrics Revolution: Scaling Part 1 - Metrics: Swallow the Red Pill Metrics: Swallow the Red Pill Part 2 - Metrics Reloaded: The Oracle Metrics Reloaded: The Oracle Part 3 - The Metrics Revolution: Scaling The Metrics Revolution: Scaling In this article, we will be discussing how to make these metrics more actionable (using the latest LLM advances) in order to improve the performance on an ongoing basis. The aim will be to keep the discussion simplified and fairly high level for everyone working in this domain. these metrics more actionable The Problem User Perceived Metrics and User Reported Metrics are 2 high-level classes of metrics we have discussed. Traditionally, the former is thought of as a system-level metric - these metrics are measured directly from logs. As a result, User Perceived Metrics are actionable by nature and hence operational. User Perceived Metrics User Perceived Metrics and User Reported Metrics User Reported Metrics User Perceived Metrics User Perceived Metrics Operational metrics are tracked on a regular basis from production logs and can be used for target setting w.r.t team wide OKRs. Operational metrics are tracked on a regular basis from production logs and can be used for target setting w.r.t team wide OKRs. However, even though User Perceived Metrics are easy to operationalize, it should be noted that these are “perceived” and not “actual” user metrics. As a result, hill-climbing on these metrics might not lead to a significant improvement in user perception of your conversational AI agent. This might lead to in-efficient management of resources if these projects span across multiple quarters. User Perceived Metrics User Perceived Metrics There needs to be a way to measure the expected impact of all performance improvements directly w.r.t User Reported Metrics. This should be treated as the “north star” impact. So, what is the problem? User Reported Metrics. User Reported Metrics. “north star” impact. So, what is the problem? “north star” impact. So, what is the problem? Direct user feedback is expected to be unstructured which is not actionable and different to operationalize. Direct user feedback is expected to be unstructured which is not actionable and different to operationalize. Detailed user-reported feedback should be unstructured by nature. If the user-reported feedback is made structured, then it can end up focusing on areas the internal team is already aware of. In addition to these, User Reported Metrics are also affected by factors such as seasonality and company perception. User Reported Metrics User Reported Metrics The impact on User Perceived Metrics can be estimated more accurately but User Reported Metrics has a lot of uncontrollable factors. The impact on User Perceived Metrics can be estimated more accurately but User Reported Metrics has a lot of uncontrollable factors. User Perceived Metrics User Perceived Metrics User Reported Metrics User Reported Metrics Solution The unstructured User Reported Feedback should be converted into a structured format that can be made actionable. There can be specific ML models trained for the purpose of converting unstructured feedback into existing system-level metrics. User Reported Feedback User Reported Feedback It should be noted that it might be more practical to use User Reported Metrics’ primary goal for “recent” user metric regressions to protect against the inherent skew in these metrics. For more horizontal long-term projects , these metrics should be used to measure the impact on user perception along with system-level metrics. User Reported Metrics’ User Reported Metrics’ “recent” user metric regressions horizontal long-term projects LLMs Are the Game Changers Now the question remains, what is the effort needed to train ML models for the specific metrics we are looking for? With the recent increase in popularity and availability of LLMs, it might be possible to use out-of-the-box APIs to convert unstructured feedback into something that can be tracked and measured similar to system-level metrics. It is important to note that with the increase in the number of tokens that LLMs can process, a lot of product-specific information can be provided as part of the “prompt” itself. As a result, off-the-shelf LLM APIs along with some prompt engineering can provide actionable User Reported Metrics. prompt engineering can provide actionable User Reported Metrics. User Reported Metrics. This provides a really fast way to assess the impact of system-level metric improvement projects on user perception which can be useful in prioritizing performance improvement projects. Even with this approach of structured User Reported Metrics, there is still room for unexpected changes. However, it can be assumed with some level of confidence, that if a specific project (aimed at improving a system-level metric) ends up positively impacting Reported Metrics, then the project is most likely actually improving user perception. User Reported Metrics, User Reported Metrics, Reported Metrics, Reported Metrics, However, there is no guarantee that all actually “good” changes will always effectively improve User Reported Metrics. As a result, it is important to use a mix of both to prioritize and evaluate performance improvement projects. User Reported Metrics. User Reported Metrics. As a result, it is important to use a mix of both to prioritize and evaluate performance improvement projects.

The Metrics Resurrections: Action! Action! Action!

মেট্রিক্স পুনরুত্থান: অ্যাকশন! কর্ম! কর্ম!

Die Wiederauferstehung der Metriken: Aktion! Aktion! Aktion!

Las resurrecciones de las métricas: ¡acción! ¡Acción! ¡Acción!

Les résurrections métriques : action ! Action! Action!

मेट्रिक्स पुनरुत्थान: कार्रवाई! कार्रवाई! कार्रवाई!

メトリクスの復活: アクション! アクション! アクション!

지표 부활: 액션! 행동! 행동!

As Ressurreições das Métricas: Ação! Ação! Ação!

Возрождение Метрики: Действие! Действие! Действие!

Metrics Resurrections: Aksiyon! Aksiyon! Aksiyon!

Sự hồi sinh của các số liệu: Hành động! Hoạt động! Hoạt động!

指标复活：行动！行动！行动！

Authors:
(1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu};
(2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu};
(3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu};
(4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu};
(5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai};
(6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu};
(7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Related Work Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset Implementation Experiments Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors 6 Experiments In this section, we evaluate WikiSP on WikiWebQuestions and demonstrate how it can be used to complement large language models such as GPT-3. 6.1 Semantic Parser Results We evaluate our model with two different answer accuracy metrics: (1) exact match (EM): the percentage of examples where the answers of the predicted SPARQL exactly match the gold answers, and (2) Macro F1 score (F1): the average F1 score for answers of each example. The evaluation results are shown in Table 1. Our approach achieves a 65.5% exact match accuracy and a 71.9% F1 score on the WWQ dataset. As a reference, the current state-of-the-art result on the original WebQuestionsSP dataset for Freebase is 78.8% F1 (Yu et al., 2023). The result was obtained with a combination of semantic parsing and retrieval. The WikiWebQuestions dataset is slightly different, as discussed above. More significantly, unlike Freebase, Wikidata does not have a fixed schema and ours is an end-to-end, seq2seq semantic parser. 6.2 Ablation Experiments 6.2.1 Entity Linking Our first ablation study evaluates the need for entity linking with ReFinED, by replacing it with simply using the LLM to detect entities as mentions. In this experiment, all entity IDs in the training data are replaced by their mentions; during inference, we map the predicted entities to their actual QIDs according to Section 3.2.2. The results show that replacing the neural entity linker with just using mentions reduces the exact match by 9.1% and the F1 score by 9.3%. This suggests that entity linking is important. 6.2.2 Allowing Mentions as Entities Our logical form is designed to recover from entity linking errors by allowing entities be specified by a mention, as an alternative to a QID. Our ablation study on this feature tested two training strategies: ReFinED. The entity linker tuples are produced by fine-tuned ReFinED, which may be missing entities in the gold target. The data show that generating unseen QIDs is needed for missing entities. Oracle. The entity linker tuples are exactly all the entities used in the gold. The model would only encounter missing QIDs at test time when ReFinED fails to generate all the necessary QIDs. The answer accuracy of the model using entity linked tuples from ReFinED (“No mentions, trained with ReFinED” in Table 2) lags by 2.3% when compared against our best model. The model using Oracle (“No mentions, trained with Oracle entities” in Table 2) lags by 3.4%. These results indicate that allowing mentions is useful for recovering from entity linking errors. 6.2.3 Names vs. IDs for Properties & Domains Our logical form replaces PIDs with property names, and domain-entity QIDs with the domain names. Here we evaluate the effectiveness of this query format. We compare our approach with the original SPARQL where all properties and entities are represented with PIDs and QIDs. Our ablation study shows that our representation with property names and domain names improves the answer accuracy by 2.0% (Table 2). This shows that LLMs can adapt to changes in query notation with finetuning, and it is easier to learn names than remembering random IDs. If we did not allow mentions in the predicted logical form, the replacement of QIDs with their names is likely to be more significant. 6.3 Complementing GPT-3 LLMs like GPT-3 can answer many questions on general knowledge correctly; however, they may also hallucinate. WWQ is representative of popular questions, so we expect GPT-3 to perform well. We use text-davinci-002 with the temperature set to 0 to evaluate GPT-3’s performance on WWQ. On the dev set of WWQ, GPT-3 answers 66.4% of the questions correctly and provides incomplete answers to 26.5% of the questions. For example, when asked “What does Obama have a degree in?”, GPT-3 correctly identifies President Obama’s political science degree, but fails to mention his law degree. In total, GPT-3 gives wrong answers to 7.1% of the questions. For this dev set, we can give definitive answers to 75.6% of the questions with WikiSP (Table 2). For the rest of the questions (24.4%), accounting for the overlap between the GPT-3 and our semantic parser’s results, the percentages of guessing correctly, incompletely, and incorrectly are at 15.2%, 5.5%, and 3.7%, respectively (Figure 2). In summary, the combination of GPT-3 and WikiSP makes it possible to give a definitive, correct, and complete answer three quarters of the time for the dev set. Users can also benefit from GPT3’s guesses the rest of the time at a 3.7% error rate, which is about half of the original error rate. 6.4 Error Analysis We analyzed the 111 examples in the WWQ dev set where the model failed. 6.4.1 Acceptable Alternative Results (18.0%) Our analysis shows that 18.0% of the “errors” can actually be deemed to be correct. Reasonable alternate answers (11.7%). In 11.7% of the cases, the model predicts an alternative interpretation to the question and returns a reasonable answer that is different from the gold. For example, the gold for question “what did Boudicca do?” uses the position held property, while the model predicts occupation property. Both are considered valid answers to the question. Reasonable alternative SPARQL but no answer was retrieved (6.3%). In another 6.3% of cases, the model predicts a reasonable alternative SPARQL, but the SPARQL returns no answer. Sometimes, since the information for the “correct” property is missing, the question is represented with a similar property. For example, since residence property is missing for Patrick Henry, the gold SPARQL for “where did Patrick Henry live?” uses place of birth instead, while our model predicts residence. 6.4.2 Errors in Entity Linking (35.1%) The biggest source of errors is entity linking. Entity linker failed to provide the correct entities in 35.1% of the failed examples. While WikiSP can potentially recover from missing entities, it cannot recover from incorrect entities. This is especially common for character roles, as some character roles have different entities for books and movies or even different series of movies. Sometimes WikiSP located the correct mention from the question, but the lookup failed. For example, the model located the mention of the event “allied invasion of France” in question “where did the allied invasion of France take place?”, but failed to find the corresponding entity from Wikidata by the name. 6.4.3 Errors Beyond Entity Linking Semantic parsing in Wikidata is challenging as there are no predefined schemas, and there are 150K domains and 3K applicable properties. Some representative mistakes include the following: Wrong property (17.1%). 17.1% of the errors are caused by predicting the wrong property. Some of the examples require background knowledge to parse. For example the answer of the question “what did martin luther king jr do in his life?” should return the value of movement, while the model predicts occupation. Properties are a challenge in Wikidata because as illustrated here which property to predict depends on the entity itself. Missing domain constraint (5.4%). Another common problem is missing the domain constraint. For example, the model correctly identifies that property shares border with should be used for question “what countries are around Egypt?”. However, it does not limit the answer to countries only, thus extra entities are returned. This paper is available on arxiv under CC 4.0 license. Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Authors: Authors: (1) Silei Xu, Computer Science Department, Stanford University Stanford, CA with equal contribution {silei@cs.stanford.edu}; (2) Shicheng Liu, Computer Science Department, Stanford University Stanford, CA with equal contribution {shicheng@cs.stanford.edu}; (3) Theo Culhane, Computer Science Department, Stanford University Stanford, CA {tculhane@cs.stanford.edu}; (4) Elizaveta Pertseva, Computer Science Department, Stanford University Stanford, CA, {pertseva@cs.stanford.edu}; (5) Meng-Hsi Wu, Computer Science Department, Stanford University Stanford, CA, Ailly.ai {jwu@ailly.ai}; (6) Sina J. Semnani, Computer Science Department, Stanford University Stanford, CA, {sinaj@cs.stanford.edu}; (7) Monica S. Lam, Computer Science Department, Stanford University Stanford, CA, {lam@cs.stanford.edu}. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Semantic Parsing for Wikidata Semantic Parsing for Wikidata WikiWebQuestions (WWQ) Dataset WikiWebQuestions (WWQ) Dataset Implementation Implementation Experiments Experiments Experiment with QALD-7 Experiment with QALD-7 Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References Conclusions, Limitations, Ethical Considerations, Acknowledgements, and References A. Examples of Recovering from Entity Linking Errors A. Examples of Recovering from Entity Linking Errors 6 Experiments In this section, we evaluate WikiSP on WikiWebQuestions and demonstrate how it can be used to complement large language models such as GPT-3. 6.1 Semantic Parser Results We evaluate our model with two different answer accuracy metrics: (1) exact match (EM): the percentage of examples where the answers of the predicted SPARQL exactly match the gold answers, and (2) Macro F1 score (F1): the average F1 score for answers of each example. The evaluation results are shown in Table 1. Our approach achieves a 65.5% exact match accuracy and a 71.9% F1 score on the WWQ dataset. As a reference, the current state-of-the-art result on the original WebQuestionsSP dataset for Freebase is 78.8% F1 (Yu et al., 2023). The result was obtained with a combination of semantic parsing and retrieval. The WikiWebQuestions dataset is slightly different, as discussed above. More significantly, unlike Freebase, Wikidata does not have a fixed schema and ours is an end-to-end, seq2seq semantic parser. 6.2 Ablation Experiments 6.2.1 Entity Linking 6.2.1 Entity Linking Our first ablation study evaluates the need for entity linking with ReFinED, by replacing it with simply using the LLM to detect entities as mentions. In this experiment, all entity IDs in the training data are replaced by their mentions; during inference, we map the predicted entities to their actual QIDs according to Section 3.2.2. The results show that replacing the neural entity linker with just using mentions reduces the exact match by 9.1% and the F1 score by 9.3%. This suggests that entity linking is important. 6.2.2 Allowing Mentions as Entities Our logical form is designed to recover from entity linking errors by allowing entities be specified by a mention, as an alternative to a QID. Our ablation study on this feature tested two training strategies: ReFinED . The entity linker tuples are produced by fine-tuned ReFinED, which may be missing entities in the gold target. The data show that generating unseen QIDs is needed for missing entities. ReFinED Oracle . The entity linker tuples are exactly all the entities used in the gold. The model would only encounter missing QIDs at test time when ReFinED fails to generate all the necessary QIDs. Oracle The answer accuracy of the model using entity linked tuples from ReFinED (“No mentions, trained with ReFinED” in Table 2) lags by 2.3% when compared against our best model. The model using Oracle (“No mentions, trained with Oracle entities” in Table 2) lags by 3.4%. These results indicate that allowing mentions is useful for recovering from entity linking errors. ReFinED Oracle 6.2.3 Names vs. IDs for Properties & Domains 6.2.3 Names vs. IDs for Properties & Domains Our logical form replaces PIDs with property names, and domain-entity QIDs with the domain names. Here we evaluate the effectiveness of this query format. We compare our approach with the original SPARQL where all properties and entities are represented with PIDs and QIDs. Our ablation study shows that our representation with property names and domain names improves the answer accuracy by 2.0% (Table 2). This shows that LLMs can adapt to changes in query notation with finetuning, and it is easier to learn names than remembering random IDs. If we did not allow mentions in the predicted logical form, the replacement of QIDs with their names is likely to be more significant. 6.3 Complementing GPT-3 LLMs like GPT-3 can answer many questions on general knowledge correctly; however, they may also hallucinate. WWQ is representative of popular questions, so we expect GPT-3 to perform well. We use text-davinci-002 with the temperature set to 0 to evaluate GPT-3’s performance on WWQ. On the dev set of WWQ, GPT-3 answers 66.4% of the questions correctly and provides incomplete answers to 26.5% of the questions. For example, when asked “What does Obama have a degree in?”, GPT-3 correctly identifies President Obama’s political science degree, but fails to mention his law degree. In total, GPT-3 gives wrong answers to 7.1% of the questions. For this dev set, we can give definitive answers to 75.6% of the questions with WikiSP (Table 2). For the rest of the questions (24.4%), accounting for the overlap between the GPT-3 and our semantic parser’s results, the percentages of guessing correctly, incompletely, and incorrectly are at 15.2%, 5.5%, and 3.7%, respectively (Figure 2). In summary, the combination of GPT-3 and WikiSP makes it possible to give a definitive, correct, and complete answer three quarters of the time for the dev set. Users can also benefit from GPT3’s guesses the rest of the time at a 3.7% error rate, which is about half of the original error rate. 6.4 Error Analysis We analyzed the 111 examples in the WWQ dev set where the model failed. 6.4.1 Acceptable Alternative Results (18.0%) 6.4.1 Acceptable Alternative Results (18.0%) Our analysis shows that 18.0% of the “errors” can actually be deemed to be correct. Reasonable alternate answers (11.7%). In 11.7% of the cases, the model predicts an alternative interpretation to the question and returns a reasonable answer that is different from the gold. For example, the gold for question “what did Boudicca do?” uses the position held property, while the model predicts occupation property. Both are considered valid answers to the question. Reasonable alternate answers (11.7%). Reasonable alternative SPARQL but no answer was retrieved (6.3%) . In another 6.3% of cases, the model predicts a reasonable alternative SPARQL, but the SPARQL returns no answer. Sometimes, since the information for the “correct” property is missing, the question is represented with a similar property. For example, since residence property is missing for Patrick Henry, the gold SPARQL for “where did Patrick Henry live?” uses place of birth instead, while our model predicts residence. Reasonable alternative SPARQL but no answer was retrieved (6.3%) 6.4.2 Errors in Entity Linking (35.1%) 6.4.2 Errors in Entity Linking (35.1%) The biggest source of errors is entity linking. Entity linker failed to provide the correct entities in 35.1% of the failed examples. While WikiSP can potentially recover from missing entities, it cannot recover from incorrect entities. This is especially common for character roles, as some character roles have different entities for books and movies or even different series of movies. Sometimes WikiSP located the correct mention from the question, but the lookup failed. For example, the model located the mention of the event “allied invasion of France” in question “where did the allied invasion of France take place?”, but failed to find the corresponding entity from Wikidata by the name. 6.4.3 Errors Beyond Entity Linking 6.4.3 Errors Beyond Entity Linking Semantic parsing in Wikidata is challenging as there are no predefined schemas, and there are 150K domains and 3K applicable properties. Some representative mistakes include the following: Wrong property (17.1%). 17.1% of the errors are caused by predicting the wrong property. Some of the examples require background knowledge to parse. For example the answer of the question “what did martin luther king jr do in his life?” should return the value of movement, while the model predicts occupation. Properties are a challenge in Wikidata because as illustrated here which property to predict depends on the entity itself. Wrong property (17.1%). Missing domain constraint (5.4%). Another common problem is missing the domain constraint. For example, the model correctly identifies that property shares border with should be used for question “what countries are around Egypt?”. However, it does not limit the answer to countries only, thus extra entities are returned. Missing domain constraint (5.4%). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating WikiSP on WikiWebQuestions: The Experiments We Ran

Authors:
(1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu);
(2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu);
(3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Table of Links Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.3 Agent Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References 3.5 Training-Testing-Trading Pipeline The "training-testing" workflow used by conventional machine learning methods falls short for financial tasks. It splits the data into training set and testing set. On the training data, users select features and tune parameters; then evaluate on the testing data. However, financial tasks will experience a simulation-to-reality gap between the testing performance and real-live market performance. Because the testing here is offline backtesting, while the users’ goal is to place orders in a real-world market. FinRL employs a “training-testing-trading" pipeline to reduce the simulation-to-reality gap. We use historical data (time series) for the “training-testing" part, which is the same as conventional machine learning tasks, and this testing period is for backtesting purpose. For the “trading" part, we use live trading APIs, such as CCXT, Alpaca, or Interactive Broker, allowing users carry out trades directly in a trading system. Therefore, FinRL directly connects with live trading APIs: 1). downloads live data, 2). feeds data to the trained DRL model and obtains the trading positions, and 3). allows users to place trades. Fig. 4 illustrates the “training-testing-trading” pipeline: Step 1). A training window to retrain an agent. Step 2). A testing window to evaluate the trained agent, while hyperparameters can be tuned iteratively. Step 3). Use the trained agent to trade in a trading window. Rolling window is used in the training-testing-trading pipeline, because the investors and portfolio managers need to retrain the model periodically as time goes ahead. FinRL provides flexible selections of rolling windows, such as monthly, quarterly, yearly windows, or by users’ specifications. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Authors: Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang ( Corresponding Author ), New York University Shanghai (christina.wang@nyu.edu). Corresponding Author Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.2 Application Layer 3.3 Agent Layer 3.3 Agent Layer 3.4 Environment Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References 5 Ecosystem of FinRL and Conclusions, and References 3.5 Training-Testing-Trading Pipeline The "training-testing" workflow used by conventional machine learning methods falls short for financial tasks. It splits the data into training set and testing set. On the training data, users select features and tune parameters; then evaluate on the testing data. However, financial tasks will experience a simulation-to-reality gap between the testing performance and real-live market performance. Because the testing here is offline backtesting, while the users’ goal is to place orders in a real-world market. FinRL employs a “training-testing-trading" pipeline to reduce the simulation-to-reality gap. We use historical data (time series) for the “training-testing" part, which is the same as conventional machine learning tasks, and this testing period is for backtesting purpose. For the “trading" part, we use live trading APIs, such as CCXT, Alpaca, or Interactive Broker, allowing users carry out trades directly in a trading system. Therefore, FinRL directly connects with live trading APIs: 1). downloads live data, 2). feeds data to the trained DRL model and obtains the trading positions, and 3). allows users to place trades. Fig. 4 illustrates the “training-testing-trading” pipeline: Step 1) . A training window to retrain an agent. Step 1) Step 2). A testing window to evaluate the trained agent, while hyperparameters can be tuned iteratively. Step 2). Step 3). Use the trained agent to trade in a trading window. Step 3). Rolling window is used in the training-testing-trading pipeline, because the investors and portfolio managers need to retrain the model periodically as time goes ahead. FinRL provides flexible selections of rolling windows, such as monthly, quarterly, yearly windows, or by users’ specifications. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How FinRL's Pipeline Enhances Trading Performance in Real-time Markets

10 Questions with Founder and CEO Brian Vallelunga at Doppler via [The Startup Founder Interview Template]. 1. What is your company in 2–5 words? Scalable secrets management for developers. 2. Why is now the time for your company to exist? Developers are increasingly dependent on numerous services, each requiring authentication through API keys, database URLs, and other sensitive secrets. Secure management of these secrets is crucial, not only to safeguard a company's reputation but more importantly, to protect the personal data of potentially millions of users from breaches. As the complexity of digital services grows, the need for robust, streamlined solutions to secure and manage these secrets becomes more pressing. Our company exists to address this vital need, ensuring that security keeps pace with innovation and protecting all stakeholders involved. 3. What do you love about your team, and why are you the ones to solve this problem? It's often said, but in our case, it genuinely applies—our team is truly unique. What sets us apart is not just the expertise or the skill each member brings, but their extraordinary passion for our mission. This passion isn't just about the work; it's about making a meaningful impact. This shared commitment has fostered one of the most cohesive and vibrant cultures I've ever had the pleasure of being part of. It's this combination of passion, culture, and talent that uniquely positions us to tackle such challenging problems effectively. We aren't just working; we're driven by a collective mission to innovate and lead in our field, ensuring we provide the best solutions. 4. If you weren’t building your startup, what would you be doing? If I weren't dedicated to building our startup, I would likely be involved in another venture within the hard tech space, particularly one that leverages artificial intelligence to accelerate research and development. AI has the potential to vastly improve efficiency and innovation across various industries, and I'm deeply interested in exploring how these technologies can solve complex problems and drive progress. 5. At the moment, how do you measure success? What are your metrics? We operate our company based on a well-defined funnel, where each stage is guided by specific input and output KPIs, each assigned to a dedicated owner. Teams responsible for a shared stage in the funnel—both input and output KPIs—are required to hold weekly meetings to continuously refine and improve their performance metrics. At the top of the funnel, we focus on awareness KPIs, such as website visits, while at the bottom, we measure success through financial-based KPIs like the Annual Recurring Revenue (ARR) from self-serve and managed sales. To set our financial objectives, we begin with annual revenue targets established by the board and CEO, which our finance team then breaks down into quarterly goals. These are further divided into monthly KPIs by reversing through the pipeline: from revenue to awareness. These monthly KPIs are reviewed weekly in meetings with the leadership team and the KPI owners to ensure alignment and prompt action on emerging challenges and opportunities. 6. In a few sentences, what do you offer to whom? We provide a top-tier, developer-centric secrets management solution tailored for medium to large-scale companies. Our platform equips engineering teams with robust tools to store, manage, and orchestrate access to their secrets across various projects, teams, and environments, enhancing both security and operational efficiency. 7. What’s most exciting about your traction to date? The most thrilling aspect of our progress is our upward trajectory into larger markets. Our core mission is to secure the world's secrets, and it is particularly the larger enterprises that hold the highest density of sensitive information worth protecting. The recognition and adoption of our platform by these substantial institutions is not only a testament to the value we provide but also highlights the crucial need for our services in safeguarding their extensive digital assets. This shift is both validating and motivating as we continue to expand our reach and impact. 8. Where do you think your growth will be next year? While I am cautious about making speculative forecasts far in advance, if pressed for an estimate, I anticipate seeing our revenue grow by approximately 2 to 4x next year. 9. Tell us about your first paying customer and revenue expectations over the next year. Our very first paying customer was Tonebase! I had the opportunity to meet the founders through mutual friends and introduced them to our product with the Chipotle Sales Method. Their support was invaluable during our formative days—they provided consistent, high-quality feedback that was instrumental in shaping the foundational aspects of our product. Their early endorsement not only boosted our confidence but also set a strong precedent for the level of engagement we strive to maintain with all our customers. As for the upcoming year, we are optimistic about our revenue growth, aiming to build on this strong base and expand our reach further. 10. What’s your biggest threat? I love this question! As a security company, our most significant threat is undoubtedly the possibility of being hacked. We dedicate an immense amount of time, resources, and energy to ensure this does not happen, going above and beyond standard security practices. Despite these rigorous efforts, the risk remains—not just for us, but also for our customers. We believe in transparency and talk openly about the proactive steps we take to mitigate potential risks. Get Interviewed by HackerNoon via the Startup Founder Template 10 Questions with Founder and CEO Brian Vallelunga at Doppler via [ The Startup Founder Interview Template ]. Brian Vallelunga Doppler The Startup Founder Interview Template 1. What is your company in 2–5 words? Scalable secrets management for developers. 2. Why is now the time for your company to exist? Developers are increasingly dependent on numerous services, each requiring authentication through API keys, database URLs, and other sensitive secrets. Secure management of these secrets is crucial, not only to safeguard a company's reputation but more importantly, to protect the personal data of potentially millions of users from breaches. As the complexity of digital services grows, the need for robust, streamlined solutions to secure and manage these secrets becomes more pressing. Our company exists to address this vital need, ensuring that security keeps pace with innovation and protecting all stakeholders involved. 3. What do you love about your team, and why are you the ones to solve this problem? It's often said, but in our case, it genuinely applies—our team is truly unique. What sets us apart is not just the expertise or the skill each member brings, but their extraordinary passion for our mission. This passion isn't just about the work; it's about making a meaningful impact. This shared commitment has fostered one of the most cohesive and vibrant cultures I've ever had the pleasure of being part of. It's this combination of passion, culture, and talent that uniquely positions us to tackle such challenging problems effectively. We aren't just working; we're driven by a collective mission to innovate and lead in our field, ensuring we provide the best solutions. 4. If you weren’t building your startup, what would you be doing? If I weren't dedicated to building our startup, I would likely be involved in another venture within the hard tech space, particularly one that leverages artificial intelligence to accelerate research and development. AI has the potential to vastly improve efficiency and innovation across various industries, and I'm deeply interested in exploring how these technologies can solve complex problems and drive progress. 5. At the moment, how do you measure success? What are your metrics? We operate our company based on a well-defined funnel, where each stage is guided by specific input and output KPIs, each assigned to a dedicated owner. Teams responsible for a shared stage in the funnel—both input and output KPIs—are required to hold weekly meetings to continuously refine and improve their performance metrics. At the top of the funnel, we focus on awareness KPIs, such as website visits, while at the bottom, we measure success through financial-based KPIs like the Annual Recurring Revenue (ARR) from self-serve and managed sales. To set our financial objectives, we begin with annual revenue targets established by the board and CEO, which our finance team then breaks down into quarterly goals. These are further divided into monthly KPIs by reversing through the pipeline: from revenue to awareness. These monthly KPIs are reviewed weekly in meetings with the leadership team and the KPI owners to ensure alignment and prompt action on emerging challenges and opportunities. 6. In a few sentences, what do you offer to whom? We provide a top-tier, developer-centric secrets management solution tailored for medium to large-scale companies. Our platform equips engineering teams with robust tools to store, manage, and orchestrate access to their secrets across various projects, teams, and environments, enhancing both security and operational efficiency. 7. What’s most exciting about your traction to date? The most thrilling aspect of our progress is our upward trajectory into larger markets. Our core mission is to secure the world's secrets, and it is particularly the larger enterprises that hold the highest density of sensitive information worth protecting. The recognition and adoption of our platform by these substantial institutions is not only a testament to the value we provide but also highlights the crucial need for our services in safeguarding their extensive digital assets. This shift is both validating and motivating as we continue to expand our reach and impact. 8. Where do you think your growth will be next year? While I am cautious about making speculative forecasts far in advance, if pressed for an estimate, I anticipate seeing our revenue grow by approximately 2 to 4x next year. 9. Tell us about your first paying customer and revenue expectations over the next year. Our very first paying customer was Tonebase! I had the opportunity to meet the founders through mutual friends and introduced them to our product with the Chipotle Sales Method. Their support was invaluable during our formative days—they provided consistent, high-quality feedback that was instrumental in shaping the foundational aspects of our product. Their early endorsement not only boosted our confidence but also set a strong precedent for the level of engagement we strive to maintain with all our customers. As for the upcoming year, we are optimistic about our revenue growth, aiming to build on this strong base and expand our reach further. 10. What’s your biggest threat? I love this question! As a security company, our most significant threat is undoubtedly the possibility of being hacked. We dedicate an immense amount of time, resources, and energy to ensure this does not happen, going above and beyond standard security practices. Despite these rigorous efforts, the risk remains—not just for us, but also for our customers. We believe in transparency and talk openly about the proactive steps we take to mitigate potential risks. Get Interviewed by HackerNoon via the Startup Founder Template Get Interviewed by HackerNoon via the Startup Founder Template

Tech Founder Interview on Scalable Secrets Management for Developers and Scaling Yearly Revenue 2-4X

বিকাশকারীদের জন্য স্কেলেবল সিক্রেটস ম্যানেজমেন্ট এবং বার্ষিক আয় 2-4X স্কেল করার বিষয়ে টেক প্রতিষ্ঠাতা সাক্ষাৎকার

Interview mit Tech-Gründer zum Thema skalierbares Geheimnismanagement für Entwickler und Steigerung des Jahresumsatzes um das Zwei- bis Vierfache

Entrevista al fundador de tecnología sobre la gestión escalable de secretos para desarrolladores y el aumento de los ingresos anuales de 2 a 4 veces

Entretien avec le fondateur technique sur la gestion évolutive des secrets pour les développeurs et la multiplication par 2 à 4 des revenus annuels

डेवलपर्स के लिए स्केलेबल सीक्रेट्स मैनेजमेंट और वार्षिक राजस्व को 2-4X तक बढ़ाने पर टेक संस्थापक का साक्षात्कार

開発者向けのスケーラブルなシークレット管理と年間収益の 2 ～ 4 倍の拡大に関する技術創業者インタビュー

개발자를 위한 확장 가능한 비밀 관리 및 연간 수익 2~4배 확장에 대한 기술 창립자 인터뷰

Entrevista com o fundador da tecnologia sobre gerenciamento escalonável de segredos para desenvolvedores e aumento da receita anual de 2 a 4 vezes

Интервью с основателем технологии о масштабируемом управлении секретами для разработчиков и увеличении годового дохода в 2–4 раза

Geliştiriciler için Ölçeklenebilir Gizli Bilgi Yönetimi ve Yıllık Gelirin 2-4 Kat Arttırılması Konusunda Teknoloji Kurucusu Röportajı

Phỏng vấn người sáng lập công nghệ về quản lý bí mật có thể mở rộng dành cho nhà phát triển và mở rộng quy mô Doanh thu hàng năm gấp 2-4 lần

技术创始人访谈：面向开发人员的可扩展机密管理以及将年收入扩大 2-4 倍

How to Build an LLM Application With Google Gemini

Google মিথুন দিয়ে কীভাবে একটি LLM অ্যাপ্লিকেশন তৈরি করবেন

So erstellen Sie eine LLM-Bewerbung mit Google Gemini

Cómo crear una aplicación LLM con Google Gemini

Comment créer une application LLM avec Google Gemini

Google Gemini के साथ LLM एप्लिकेशन कैसे बनाएं

Stories, News & Mentions about QUARO

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