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Amazon is back at it again, trying to convince us that mixing retail with reality TV is the future we’ve all been waiting for. Their latest venture, “Buy It Now Bonanza,” launching October 30th, is basically a lovechild of Shark Tank and a hyperactive QVC on espresso shots who’s been blessed by Billy Mays. Entrepreneurs will strut out onto the stage and pitch their products with the desperation of someone pitching their business in a doomsday bunker for the chance to be featured in a dedicated “Buy It Now” store on Amazon and $20k in cash. The differentiating twist? You, dear viewer, can buy these products live as the show unfolds. Yep, you’re now part of the equation. Lauren Anderson, Amazon MGM Studios’ head of brand and content innovation, says the show is “all about giving audiences a glimpse into Amazon’s collaborative process with small businesses”, and “an opportunity for Amazon to showcase how they work with entrepreneurs.”  (So, a nicer way of saying: “Look, we’re not just a digital jungle where products go to get lost!”) But let’s peel back the glittery PR gloss and see it for what it is: t-commerce (television commerce) on a sugar rush, primed to turn your TV into a 24/7 carnival of consumerism. Let’s pause for a history lesson. T-commerce isn’t some new-fangled tech bro gimmick; it’s been around since the days when shoulder pads were in fashion and Madonna was still scandalous. It’s just an umbrella term that covers every possible way a person can be seduced into spending money from their couch. Remember QVC? That wasn’t just your grandma’s background noise while she knit itchy scarves. That and late-night infomercials were the pilgrims of t-commerce, turning casual TV watching into a full-contact sport where the prize was a set of steak knives or a fitness gadget that doubled as a coat rack. But fast-forward to the 2020s, and t-commerce has had a glow-up, and spawned an offspring known as shoppable TV. The main difference is that shoppable TV refers specifically to when sophisticated tech turns any TV into a virtual checkout lane. Take LG, for example, which integrated a platform called “LG Channels” that allows viewers to click on interactive ads and purchase products directly from their screens. Then there’s Walmart, which partnered with NBC Universal to launch a shoppable TV experience that transformed shows like Today into seamless shopping opportunities. By scanning a QR code or clicking a button on their remote, viewers could add products to their Walmart cart without ever leaving the couch. It’s a new era where your favorite sitcom can double as a personalized shopping mall—minus the mall walkers and pretzel stands. But whether it’s calling a number splashed across the screen in a retro cable ad or scanning a QR code while half-watching a reality show, the goal is the same: to turn your TV into a giant, digital impulse-buy button. You see it, you want it, you click it, it’s yours. It’s the lazy shopaholic’s dream—or nightmare, depending on your credit card limit. And Amazon? They’ve been trying to perfect this blend of entertainment and shopping for years, but with the success of Prime Video and the constant buzzing drone of Alexa in our homes, they’ve finally got the audience hooked. “Buy It Now Bonanza” isn’t their first attempt to turn TV into a digital mall. Remember those awkward QR codes popping up during shows, urging you to whip out your phone and buy some obscure kitchen gadget? Or that ill-fated Style Code Live that fizzled out in less than a year? Yes, we’ve seen this dance before. But Amazon’s nothing if not persistent, and like a toddler with a bag of sugar, they just keep going. So why could this be the moment they get it right? Well, for one thing, it’s not just about shopping—it’s about the spectacle. You’re not being asked to buy a product; you’re being lured into an emotional narrative. You’ll watch an entrepreneur put their heart, soul, and probably a second mortgage on the line, while Amazon executives and Gwyneth Paltrow (because, why not?) decide their fate. It’s high stakes, it’s drama, and if produced well, it’s the ultimate feel-good (or cringe) binge. And that’s where the genius lies. Because as you’re drawn into the tension and drama, you’ll almost forget that you’re being sold something. Almost. But the real strategy here is even sneakier. Amazon doesn’t just want your money; they want your future. This show is their latest attempt to reposition themselves not just as a place to find the cheapest toilet paper, but as a destination for discovering the cool, quirky, and innovative. They’ve been struggling to move from being the “everything store” to the “inspiration store,” but it’s tough to inspire when your algorithm keeps pushing bulk toothpaste. This show is a bet that by packaging product discovery in the slick, shiny wrapping paper of reality TV, they might finally convince you that they’re more than just an online bazaar. Yet, despite all the polish, there’s a part of us that wonders: isn’t this just a really, really long ad? But…does that really matter if it’s entertaining enough? Maybe not. Even Bernstein analyst Mark Shmulik quipped, “This feels more elegant than QR codes.” At the end of the day, if “Buy It Now Bonanza” manages to pull us away from doom-scrolling through social media or pretending to be interested in another AI-generated news article, maybe we’ll forgive Amazon for turning our TVs into a giant checkout screen. After all, we’ve already given in to binge-watching; is binge-buying really that far behind? And with that, perhaps it’s refreshing to focus on a piece of tech news that, for once, has nothing to do with AI. Well, unless Alexa decides to join the panel next season. Amazon is back at it again, trying to convince us that mixing retail with reality TV is the future we’ve all been waiting for. Their latest venture, “Buy It Now Bonanza,” launching October 30th, is basically a lovechild of Shark Tank and a hyperactive QVC on espresso shots who’s been blessed by Billy Mays. a lovechild of Shark Tank and a hyperactive QVC on espresso shots who’s been blessed by Billy Mays. Entrepreneurs will strut out onto the stage and pitch their products with the desperation of someone pitching their business in a doomsday bunker for the chance to be featured in a dedicated “Buy It Now” store on Amazon and $20k in cash. The differentiating twist? You, dear viewer, can buy these products live as the show unfolds. The differentiating twist? You, dear viewer, can buy these products live as the show unfolds. live Yep, you’re now part of the equation. Yep, you’re now part of the equation. Yep, you’re now part of the equation. Lauren Anderson, Amazon MGM Studios’ head of brand and content innovation, says the show is “all about giving audiences a glimpse into Amazon’s collaborative process with small businesses”, and “an opportunity for Amazon to showcase how they work with entrepreneurs.”  (So, a nicer way of saying: “Look, we’re not just a digital jungle where products go to get lost!”) But let’s peel back the glittery PR gloss and see it for what it is: t-commerce (television commerce) on a sugar rush, primed to turn your TV into a 24/7 carnival of consumerism. But let’s peel back the glittery PR gloss and see it for what it is: t-commerce (television commerce) on a sugar rush, primed to turn your TV into a 24/7 carnival of consumerism . primed to turn your TV into a 24/7 carnival of consumerism primed to turn your TV into a 24/7 carnival of consumerism Let’s pause for a history lesson. T-commerce isn’t some new-fangled tech bro gimmick; it’s been around since the days when shoulder pads were in fashion and Madonna was still scandalous. It’s just an umbrella term that covers every possible way a person can be seduced into spending money from their couch . Remember QVC? That wasn’t just your grandma’s background noise while she knit itchy scarves. T-commerce isn’t some new-fangled tech bro gimmick; it’s been around since the days when shoulder pads were in fashion and Madonna was still scandalous. every possible way a person can be seduced into spending money from their couch every possible way a person can be seduced into spending money from their couch That and late-night infomercials were the pilgrims of t-commerce, turning casual TV watching into a full-contact sport where the prize was a set of steak knives or a fitness gadget that doubled as a coat rack. But fast-forward to the 2020s, and t-commerce has had a glow-up, and spawned an offspring known as shoppable TV . The main difference is that shoppable TV refers specifically to when sophisticated tech turns any TV into a virtual checkout lane. Take LG, for example, which integrated a platform called “LG Channels” that allows viewers to click on interactive ads and purchase products directly from their screens. shoppable TV shoppable TV shoppable TV Then there’s Walmart, which partnered with NBC Universal to launch a shoppable TV experience that transformed shows like Today into seamless shopping opportunities. By scanning a QR code or clicking a button on their remote, viewers could add products to their Walmart cart without ever leaving the couch. It’s a new era where your favorite sitcom can double as a personalized shopping mall—minus the mall walkers and pretzel stands. But whether it’s calling a number splashed across the screen in a retro cable ad or scanning a QR code while half-watching a reality show, the goal is the same: to turn your TV into a giant, digital impulse-buy button. You see it, you want it, you click it, it’s yours. It’s the lazy shopaholic’s dream—or nightmare, depending on your credit card limit. But whether it’s calling a number splashed across the screen in a retro cable ad or scanning a QR code while half-watching a reality show, the goal is the same: to turn your TV into a giant, digital impulse-buy button . You see it, you want it, you click it, it’s yours. It’s the lazy shopaholic’s dream—or nightmare, depending on your credit card limit. to turn your TV into a giant, digital impulse-buy button You see it, you want it, you click it, it’s yours. And Amazon? They’ve been trying to perfect this blend of entertainment and shopping for years, but with the success of Prime Video and the constant buzzing drone of Alexa in our homes, they’ve finally got the audience hooked. “Buy It Now Bonanza” isn’t their first attempt to turn TV into a digital mall. And Amazon? They’ve been trying to perfect this blend of entertainment and shopping for years, but with the success of Prime Video and the constant buzzing drone of Alexa in our homes, they’ve finally got the audience hooked. finally Remember those awkward QR codes popping up during shows, urging you to whip out your phone and buy some obscure kitchen gadget? Or that ill-fated Style Code Live that fizzled out in less than a year? Yes, we’ve seen this dance before. But Amazon’s nothing if not persistent, and like a toddler with a bag of sugar, they just keep going. But Amazon’s nothing if not persistent, and like a toddler with a bag of sugar, they just keep going. So why could this be the moment they get it right? So why could this be the moment they get it right? Well, for one thing, it’s not just about shopping—it’s about the spectacle. You’re not being asked to buy a product; you’re being lured into an emotional narrative . You’ll watch an entrepreneur put their heart, soul, and probably a second mortgage on the line, while Amazon executives and Gwyneth Paltrow (because, why not?) decide their fate. Well, for one thing, it’s not just about shopping—it’s about the spectacle. Well, for one thing, it’s not just about shopping—it’s about the spectacle. asked you’re being lured into an emotional narrative It’s high stakes, it’s drama, and if produced well, it’s the ultimate feel-good (or cringe) binge. And that’s where the genius lies. Because as you’re drawn into the tension and drama, you’ll almost forget that you’re being sold something. Almost. Because as you’re drawn into the tension and drama, you’ll almost forget that you’re being sold something. Almost. Because as you’re drawn into the tension and drama, you’ll almost forget that you’re being sold something. Almost. But the real strategy here is even sneakier. Amazon doesn’t just want your money; they want your future. This show is their latest attempt to reposition themselves not just as a place to find the cheapest toilet paper, but as a destination for discovering the cool, quirky, and innovative. But the real strategy here is even sneakier. Amazon doesn’t just want your money; they want your future. Amazon doesn’t just want your money; they want your future . future destination for discovering destination for discovering discovering They’ve been struggling to move from being the “everything store” to the “inspiration store,” but it’s tough to inspire when your algorithm keeps pushing bulk toothpaste. This show is a bet that by packaging product discovery in the slick, shiny wrapping paper of reality TV, they might finally convince you that they’re more than just an online bazaar. Yet, despite all the polish, there’s a part of us that wonders: isn’t this just a really, really long ad? But…does that really matter if it’s entertaining enough? Maybe not. Even Bernstein analyst Mark Shmulik quipped, “This feels more elegant than QR codes.” Yet, despite all the polish, there’s a part of us that wonders: isn’t this just a really, really long ad? really At the end of the day, if “Buy It Now Bonanza” manages to pull us away from doom-scrolling through social media or pretending to be interested in another AI-generated news article, maybe we’ll forgive Amazon for turning our TVs into a giant checkout screen. After all, we’ve already given in to binge-watching; is binge-buying really that far behind? After all, we’ve already given in to binge-watching; is binge-buying really that far behind? really And with that, perhaps it’s refreshing to focus on a piece of tech news that, for once, has nothing to do with AI. Well, unless Alexa decides to join the panel next season. And with that, perhaps it’s refreshing to focus on a piece of tech news that, for once, has nothing to do with AI. Well, unless Alexa decides to join the panel next season.

Amazon’s ‘Buy It Now Bonanza’: The Shoppable Spectacle That’s Out to Make Your Wallet Weep

SINGAPORE — In what many are calling the boldest move in crypto innovation, the latest stablecoin, "USDN" (short for US Deez Nuts), launched at Token2049 with the promise of being a rock-solid, man-pegged asset. Unfortunately, the coin’s value plummeted to zero following sudden post-event clarity, leaving investors grasping for answers—and their wallets. Editor’s note: This article is satire and a creation of the author’s mind. “We wanted a stablecoin that truly represented the resilience of the U.S. dollar,” said USDN's founder, Chad "Diamond Hands" Johnson, wearing a shirt emblazoned with the phrase "Got Nuts?" on the back. “But also, we wanted it to be fun. You know, like a meme, but serious. That’s where the name came from.” Initially, the coin enjoyed rapid adoption, with hundreds of Token2049 attendees eagerly trading their assets for USDN in the hopes of escaping the volatility of other cryptocurrencies. "I wanted a safe haven for my portfolio," said one trader, moments after swapping his Ethereum for USDN. "Plus, the name is hilarious." However, within hours of launch, USDN's value began to wobble. Investors grew concerned when they discovered that the coin’s stability was, in fact, pegged not to the U.S. dollar but to a complex algorithm based on "vibes" and "hype metrics" from crypto Twitter. “We were blindsided,” admitted Johnson, sipping on a $12 coconut water purchased with real fiat money. “Turns out, pegging a currency to Twitter memes wasn’t the most solid foundation. Who could’ve seen that coming?” As the coin’s value collapsed from $1 to $0.69, then swiftly to $0.00, Token2049 attendees realized they had been left holding a metaphorical bag—not of nuts, but of regrets. The USDN Discord server, once buzzing with “Deez Nuts” jokes and GIFs, quickly turned into a hotbed of panic and desperation. Still, some remained optimistic. “I’m just gonna HODL,” said one determined trader, who refused to sell his now-worthless USDN holdings. “I believe in the long-term vision. Deez nuts always bounce back.” Johnson, unfazed by the collapse, assured investors that USDN would return stronger than ever with a rebranding effort. “Next time, we’ll just call it Deez Nutscoin,” he said. “I think people will really rally behind that. It’s not about the money, it’s about the memes.” In the aftermath of the collapse, the crypto community reflected on the lessons learned from USDN’s spectacular failure. Chief among them: maybe not every meme needs to be a financial product. SINGAPORE — In what many are calling the boldest move in crypto innovation, the latest stablecoin, "USDN" (short for US Deez Nuts), launched at Token2049 with the promise of being a rock-solid, man-pegged asset. Unfortunately, the coin’s value plummeted to zero following sudden post-event clarity, leaving investors grasping for answers—and their wallets. Editor’s note: This article is satire and a creation of the author’s mind. Editor’s note: This article is satire and a creation of the author’s mind. Editor’s note: This article is satire and a creation of the author’s mind. Editor’s note: This article is satire and a creation of the author’s mind. “We wanted a stablecoin that truly represented the resilience of the U.S. dollar,” said USDN's founder, Chad "Diamond Hands" Johnson, wearing a shirt emblazoned with the phrase "Got Nuts?" on the back. “But also, we wanted it to be fun. You know, like a meme, but serious. That’s where the name came from.” Initially, the coin enjoyed rapid adoption, with hundreds of Token2049 attendees eagerly trading their assets for USDN in the hopes of escaping the volatility of other cryptocurrencies. "I wanted a safe haven for my portfolio," said one trader, moments after swapping his Ethereum for USDN. "Plus, the name is hilarious." However, within hours of launch, USDN's value began to wobble. Investors grew concerned when they discovered that the coin’s stability was, in fact, pegged not to the U.S. dollar but to a complex algorithm based on "vibes" and "hype metrics" from crypto Twitter. “We were blindsided,” admitted Johnson, sipping on a $12 coconut water purchased with real fiat money. “Turns out, pegging a currency to Twitter memes wasn’t the most solid foundation. Who could’ve seen that coming?” As the coin’s value collapsed from $1 to $0.69, then swiftly to $0.00, Token2049 attendees realized they had been left holding a metaphorical bag—not of nuts, but of regrets. The USDN Discord server, once buzzing with “Deez Nuts” jokes and GIFs, quickly turned into a hotbed of panic and desperation. Still, some remained optimistic. “I’m just gonna HODL,” said one determined trader, who refused to sell his now-worthless USDN holdings. “I believe in the long-term vision. Deez nuts always bounce back.” Johnson, unfazed by the collapse, assured investors that USDN would return stronger than ever with a rebranding effort. “Next time, we’ll just call it Deez Nutscoin,” he said. “I think people will really rally behind that. It’s not about the money, it’s about the memes.” In the aftermath of the collapse, the crypto community reflected on the lessons learned from USDN’s spectacular failure. Chief among them: maybe not every meme needs to be a financial product.

US Dollar-Pegged "USDN" (US Deez Nuts) Promises To Penetrate Volatile Market, Fails Spectacularly

Το "USDN" (USDN) που συνδέεται με το δολάριο ΗΠΑ υπόσχεται να διεισδύσει στην ευμετάβλητη αγορά, αποτυγχάνει θεαματικά

El "USDN" (US Deez Nuts), vinculado al dólar estadounidense, promete penetrar en un mercado volátil, pero fracasa espectacularmente

L'USDN (US Deez Nuts), indexé sur le dollar américain, promet de pénétrer un marché volatil, mais échoue lamentablement

"USDN" (US Deez Nuts) המוצמד לדולר ארה"ב מבטיח לחדור לשוק הפכפך, נכשל בצורה מרהיבה

अमेरिकी डॉलर से जुड़े "यूएसडीएन" (यूएस डीज़ नट्स) ने अस्थिर बाजार में पैठ बनाने का वादा किया, लेकिन यह विफल हो गया

"USDN" (US Deez Nuts) ki fikse sou dola ameriken pwomèt pou l antre nan mache temèt, echwe espektakilèman.

ԱՄՆ դոլարով կապված «USDN»-ը (ԱՄՆ Deez Nuts) խոստանում է ներթափանցել անկայուն շուկա, տպավորիչ կերպով ձախողվել է

"USDN" (US Deez Nuts) yang Dipatok pada Dolar AS Berjanji Menembus Pasar yang Bergejolak, Namun Gagal Total

Il dollaro statunitense "USDN" (US Deez Nuts) promette di penetrare il mercato volatile, ma fallisce clamorosamente

米ドルにペッグされた「USDN」（US Deez Nuts）は不安定な市場に浸透すると約束したが、見事に失敗した

ASV dolāram piesaistītais "USDN" (ASV Deez Nuts) sola iekļūt nestabilajā tirgū, iespaidīgi neizdodas

„USDN“ поврзан со американски долари (US Deez Nuts) ветува дека ќе навлезе во нестабилниот пазар, спектакуларно не успеа

(1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK;
(2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK;
(5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction
2 Methodology
2.1 Local spectral irradiance as a function of stellar temperature
2.2 Thermodynamic model of an oxygenic light-harvesting system
2.3 Lattice model of an oxygenic light-harvesting system
3 Results
3.1 Orbital distances and incident spectral fluxes
3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR
3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR
3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’
4 Discussion
Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 3 Results 3.1 Orbital distances and incident spectral fluxes This paper is available on arxiv under CC 4.0 license. (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Abstract and Introduction Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 3 Results 3.1 Orbital distances and incident spectral fluxes 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

Thermodynamic Limits Around M-dwarf Stars: Orbital Distances and Incident Spectral Fluxes

(1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK;
(2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK;
(5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction
2 Methodology
2.1 Local spectral irradiance as a function of stellar temperature
2.2 Thermodynamic model of an oxygenic light-harvesting system
2.3 Lattice model of an oxygenic light-harvesting system
3 Results
3.1 Orbital distances and incident spectral fluxes
3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR
3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR
3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’
4 Discussion
Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature As in previous work [Duffy et al., 2023], we use stellar spectral models for stars of different effective temperatures, Ts, generated by the phoenix code [Husser, T.-O. et al., 2013]. To reduce expense of subsequent numerical integration calculations, we smooth and re-sample the spectrum down to 4000 points, which still captures the large scale features. The absorption profiles of typical light-harvesting pigments are quite broad, with a Full Width at Half Maximum (FWHM) > 10 nm. Photon absorption by these pigments therefore integrates over this wavelength range (see next subsection), meaning that the photosynthetic pigments can only resolve spectral details on scales comparable to or larger than their own absorption line-width. Even with this smoothing, spectral resolution is well below this limit. This paper is available on arxiv under CC 4.0 license. (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Abstract and Introduction Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature As in previous work [Duffy et al., 2023], we use stellar spectral models for stars of different effective temperatures, Ts, generated by the phoenix code [Husser, T.-O. et al., 2013]. To reduce expense of subsequent numerical integration calculations, we smooth and re-sample the spectrum down to 4000 points, which still captures the large scale features. The absorption profiles of typical light-harvesting pigments are quite broad, with a Full Width at Half Maximum (FWHM) > 10 nm. Photon absorption by these pigments therefore integrates over this wavelength range (see next subsection), meaning that the photosynthetic pigments can only resolve spectral details on scales comparable to or larger than their own absorption line-width. Even with this smoothing, spectral resolution is well below this limit. 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

Thermodynamic Limits Around M-dwarf Stars: Local Spectral Irradiance

Author:
(1) Ahmed Farag Ali, Essex County College and Department of Physics, Faculty of Science, Benha University. Table of Links Abstract and Introduction Space-time quanta and Becken Universal bound Shape of space-time quanta Symmetry of space-time quanta Space-time quanta and Spectral mass gap Phenomenological implications Conclusion, Acknowledgments, and References V. SPACE-TIME QUANTA AND SPECTRAL MASS GAP The solution of the mass gap problem as described in [62] requires proving that Yang-Mills theory exists and that the mass of all particles predicted by the theory is strictly positive. Both conditions are satisfied by the space-time quanta. According to Eq. (9), we find that the mass of space-time quanta is a real positive value. The space-time quanta is quatified by the parameter κ in Snyder algebra that was found to generate both non-commutative geometry and GUP at the same time. For Non-commutative geometry part, it is found to emerge naturally at limits of M/string theory [2] as higher dimensional corrections of ordinary Yang-Mills theory [3]. Since the space-time is locally flat, the space-time quanta must be described by a 4-polytope. We introduce a geometric and symmetric reason to consider the 24-cell as the space-time quanta. The most important reason is that 24-cell is the Weyl/Coxeter group of F4 group that can generate the standard model gauge symmetry as shown in recent studies [45]. Therefore, Yang-Mill’s theory exists as a F4 group and is explained by the space-time quanta from the first geometric principles as 24-cell. The gluon masses should be related to the 16-cell which is related to the 24-cell we explained in previous sections. We conclude that the space-time quanta introduces a geometric origin of the spectral mass gap [62]. The spectral mass gap is entirely determined by the length/radius of 24-cell according to Eq. (9). Recently, it was shown that spectral gaps exist in Hamiltonian with quasicrystal line order [63]. Quasicrystal considerations in Holography, the basic structure of nature, and cosmology are discussed in [64–71]. We think the quantum space-time may be a quasicrystal with a fundamental structure of a 24-cell. Experimental observations of quantum time quasicrystal are reported in [72]. This quasicrystal order is expected to follow from simulating Snyder’s algebra with considering 24-cell as its fundamental structure. This needs further investigation. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Ahmed Farag Ali, Essex County College and Department of Physics, Faculty of Science, Benha University. Author: Author: (1) Ahmed Farag Ali, Essex County College and Department of Physics, Faculty of Science, Benha University. Table of Links Abstract and Introduction Abstract and Introduction Space-time quanta and Becken Universal bound Space-time quanta and Becken Universal bound Shape of space-time quanta Shape of space-time quanta Symmetry of space-time quanta Symmetry of space-time quanta Space-time quanta and Spectral mass gap Space-time quanta and Spectral mass gap Phenomenological implications Phenomenological implications Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References V. SPACE-TIME QUANTA AND SPECTRAL MASS GAP The solution of the mass gap problem as described in [62] requires proving that Yang-Mills theory exists and that the mass of all particles predicted by the theory is strictly positive. Both conditions are satisfied by the space-time quanta. According to Eq. (9), we find that the mass of space-time quanta is a real positive value. The space-time quanta is quatified by the parameter κ in Snyder algebra that was found to generate both non-commutative geometry and GUP at the same time. For Non-commutative geometry part, it is found to emerge naturally at limits of M/string theory [2] as higher dimensional corrections of ordinary Yang-Mills theory [3]. Since the space-time is locally flat, the space-time quanta must be described by a 4-polytope. We introduce a geometric and symmetric reason to consider the 24-cell as the space-time quanta. The most important reason is that 24-cell is the Weyl/Coxeter group of F4 group that can generate the standard model gauge symmetry as shown in recent studies [45]. Therefore, Yang-Mill’s theory exists as a F4 group and is explained by the space-time quanta from the first geometric principles as 24-cell. The gluon masses should be related to the 16-cell which is related to the 24-cell we explained in previous sections. We conclude that the space-time quanta introduces a geometric origin of the spectral mass gap [62]. The spectral mass gap is entirely determined by the length/radius of 24-cell according to Eq. (9). Recently, it was shown that spectral gaps exist in Hamiltonian with quasicrystal line order [63]. Quasicrystal considerations in Holography, the basic structure of nature, and cosmology are discussed in [64–71]. We think the quantum space-time may be a quasicrystal with a fundamental structure of a 24-cell. Experimental observations of quantum time quasicrystal are reported in [72]. This quasicrystal order is expected to follow from simulating Snyder’s algebra with considering 24-cell as its fundamental structure. This needs further investigation. 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

Space-Time Quanta and the Spectral Mass Gap

Authors:
(1) Troisemaine Colin, Department of Computer Science, IMT Atlantique, Brest, France., and Orange Labs, Lannion, France;
(2) Reiffers-Masson Alexandre, Department of Computer Science, IMT Atlantique, Brest, France.;
(3) Gosselin Stephane, Orange Labs, Lannion, France;
(4) Lemaire Vincent, Orange Labs, Lannion, France;
(5) Vaton Sandrine, Department of Computer Science, IMT Atlantique, Brest, France. Table of Links Abstract and Intro Related work Approaches Hyperparameter optimization Estimating the number of novel classes Full training procedure Experiments Conclusion Declarations References Appendix A: Additional result metrics Appendix B: Hyperparameters Appendix C: Cluster Validity Indices numerical results Appendix D: NCD k-means centroids convergence study 3 Approaches In this section, after introducing the notations, we define two simple but potentially strong models derived from classical clustering algorithms (Sections 3.2 and 3.3). The idea is to use the labeled data to improve the unsupervised clustering process, and make the comparison to NCD methods more challenging. Then, we present a new method, PBN (for Projection-Based NCD, Section 3.4), characterized by its low number of hyperparameters needed to be tuned. 3.1 Problem setting 3.2 NCD k-means This is a straightforward method that takes inspiration from k-means++ [24], which is an algorithm for choosing the initial positions of the centroids (or cluster centers). In k-means++, the first centroid is chosen at random from the data points. Then, each new centroid is chosen iteratively from one of the data points with a probability proportional to the squared distance from the point’s closest centroid. The resulting initial positions of the centroids are generally spread more evenly, which yields appreciable improvement in the final error of k-means and convergence time. Similarly to k-means++, we repeat the initialization process a few times and keep the centroids that achieved the smallest inertia. Note that, to stay consistent with the k-means algorithm, we use also the L2 norm (i.e. the Euclidean distance) for NCD k-means. 3.3 NCD Spectral Clustering To incorporate the knowledge from the known classes in the Spectral Clustering process, our initial approach was to utilize NCD k-means within the spectral embedding. In short, we would initially compute the full spectral embedding for all the data and determine the mean points of the known classes with the help of the ground truth labels. These mean points would then serve as the initial centroids. However, the observed performance improvement over the fully unsupervised SC was quite marginal. Instead, the idea that we will use throughout this article (and that we refer to as “NCD Spectral Clustering”) stems from the observation that SC can obtain very different results according to the parameters that are used. Among these parameters, the temperature parameter σ of the kernel holds particular importance, as it directly impacts the adjacency matrix’s accuracy in representing the neighborhood relationships of the data points. The rule of thumb of Equation 1 still requires to choose a value, but significantly reduces the space of possible values. Additionally, while the literature often sets the number of components u of the spectral embedding equal to the number of clusters, we have observed that optimizing it can also improve performance. Therefore, rather than a specific method, we propose the parameter optimization scheme illustrated in Figure 2. For a given combination of parameters {smin, u}, the corresponding spectral embedding of all the data is computed and then partitioned with k-means. The quality of the parameters is evaluated from the clustering performance on the known classes, as the ground-truth labels are only available for the known classes. Indeed an important hypothesis behind the NCD setup is that the known and novel classes are related and share similarities, so they should have similar feature scales and distributions. Consequently, if the Spectral Clustering performs well on the known classes, the parameters are likely suitable to represent the novel classes. Discussion. This idea can be applied to optimize the parameters of any unsupervised clustering algorithm in the NCD context. For example, the Eps and M inP ts parameters of DBSCAN [29] can be selected in the same manner. It is also possible to use a different adjacency matrix in the SC algorithm. One option could be to substitute the Gaussian kernel with the k-nearest neighbor graph, and therefore optimise k instead of σ. However, for the sake of simplicity, we will only investigate SC using the Gaussian kernel. 3.4 Projection-Based NCD The cross-entropy loss on the known classes is defined as: The reconstruction loss of the instances from all classes is written as: Projection-based NCD requires tuning of four hyperparameters. The trade-off parameter w is inherent to the method and the other three come from the choice of architecture: the learning rate, the dropout rate and the size of the latent space. Note that this method doesn’t employ complex schemes to define pseudo-labels unlike many NCD works. They have been proven to be accurate with image data (notably thanks to data augmentation techniques) [4, 31], but we found in preliminary results not detailed here, that for tabular data, they introduce variability in the results and new hyperparameters that need to be tuned. 3.5 Summary of proposed approaches In the next section, we present an approach to finding hyperparameters without using the labels of the novel classes, which are not available in realistic scenarios. Indeed in the experiments (see Section 7), it should become clear why the simplicity of the proposed approach is a desirable feature for hyperparameter optimization in the NCD context. This paper is available on arxiv under CC 4.0 license. Authors: (1) Troisemaine Colin, Department of Computer Science, IMT Atlantique, Brest, France., and Orange Labs, Lannion, France; (2) Reiffers-Masson Alexandre, Department of Computer Science, IMT Atlantique, Brest, France.; (3) Gosselin Stephane, Orange Labs, Lannion, France; (4) Lemaire Vincent, Orange Labs, Lannion, France; (5) Vaton Sandrine, Department of Computer Science, IMT Atlantique, Brest, France. Authors: Authors: (1) Troisemaine Colin, Department of Computer Science, IMT Atlantique, Brest, France., and Orange Labs, Lannion, France; (2) Reiffers-Masson Alexandre, Department of Computer Science, IMT Atlantique, Brest, France.; (3) Gosselin Stephane, Orange Labs, Lannion, France; (4) Lemaire Vincent, Orange Labs, Lannion, France; (5) Vaton Sandrine, Department of Computer Science, IMT Atlantique, Brest, France. Table of Links Abstract and Intro Abstract and Intro Related work Related work Approaches Approaches Hyperparameter optimization Hyperparameter optimization Estimating the number of novel classes Estimating the number of novel classes Full training procedure Full training procedure Experiments Experiments Conclusion Conclusion Declarations Declarations References References Appendix A: Additional result metrics Appendix A: Additional result metrics Appendix B: Hyperparameters Appendix B: Hyperparameters Appendix C: Cluster Validity Indices numerical results Appendix C: Cluster Validity Indices numerical results Appendix D: NCD k-means centroids convergence study Appendix D: NCD k-means centroids convergence study 3 Approaches In this section, after introducing the notations, we define two simple but potentially strong models derived from classical clustering algorithms (Sections 3.2 and 3.3). The idea is to use the labeled data to improve the unsupervised clustering process, and make the comparison to NCD methods more challenging. Then, we present a new method, PBN (for Projection-Based NCD, Section 3.4), characterized by its low number of hyperparameters needed to be tuned. 3.1 Problem setting 3.2 NCD k-means This is a straightforward method that takes inspiration from k-means++ [24], which is an algorithm for choosing the initial positions of the centroids (or cluster centers). In k-means++, the first centroid is chosen at random from the data points. Then, each new centroid is chosen iteratively from one of the data points with a probability proportional to the squared distance from the point’s closest centroid. The resulting initial positions of the centroids are generally spread more evenly, which yields appreciable improvement in the final error of k-means and convergence time. Similarly to k-means++, we repeat the initialization process a few times and keep the centroids that achieved the smallest inertia. Note that, to stay consistent with the k-means algorithm, we use also the L2 norm (i.e. the Euclidean distance) for NCD k-means. 3.3 NCD Spectral Clustering To incorporate the knowledge from the known classes in the Spectral Clustering process, our initial approach was to utilize NCD k-means within the spectral embedding. In short, we would initially compute the full spectral embedding for all the data and determine the mean points of the known classes with the help of the ground truth labels. These mean points would then serve as the initial centroids. However, the observed performance improvement over the fully unsupervised SC was quite marginal. Instead, the idea that we will use throughout this article (and that we refer to as “NCD Spectral Clustering”) stems from the observation that SC can obtain very different results according to the parameters that are used. Among these parameters, the temperature parameter σ of the kernel holds particular importance, as it directly impacts the adjacency matrix’s accuracy in representing the neighborhood relationships of the data points. The rule of thumb of Equation 1 still requires to choose a value, but significantly reduces the space of possible values. Additionally, while the literature often sets the number of components u of the spectral embedding equal to the number of clusters, we have observed that optimizing it can also improve performance. Therefore, rather than a specific method, we propose the parameter optimization scheme illustrated in Figure 2. For a given combination of parameters {smin, u}, the corresponding spectral embedding of all the data is computed and then partitioned with k-means. The quality of the parameters is evaluated from the clustering performance on the known classes, as the ground-truth labels are only available for the known classes. Indeed an important hypothesis behind the NCD setup is that the known and novel classes are related and share similarities, so they should have similar feature scales and distributions. Consequently, if the Spectral Clustering performs well on the known classes, the parameters are likely suitable to represent the novel classes. Discussion . This idea can be applied to optimize the parameters of any unsupervised clustering algorithm in the NCD context. For example, the Eps and M inP ts parameters of DBSCAN [29] can be selected in the same manner. It is also possible to use a different adjacency matrix in the SC algorithm. One option could be to substitute the Gaussian kernel with the k-nearest neighbor graph, and therefore optimise k instead of σ. However, for the sake of simplicity, we will only investigate SC using the Gaussian kernel. Discussion 3.4 Projection-Based NCD The cross-entropy loss on the known classes is defined as: The reconstruction loss of the instances from all classes is written as: Projection-based NCD requires tuning of four hyperparameters. The trade-off parameter w is inherent to the method and the other three come from the choice of architecture: the learning rate, the dropout rate and the size of the latent space. Note that this method doesn’t employ complex schemes to define pseudo-labels unlike many NCD works. They have been proven to be accurate with image data (notably thanks to data augmentation techniques) [4, 31], but we found in preliminary results not detailed here, that for tabular data, they introduce variability in the results and new hyperparameters that need to be tuned. 3.5 Summary of proposed approaches In the next section, we present an approach to finding hyperparameters without using the labels of the novel classes, which are not available in realistic scenarios. Indeed in the experiments (see Section 7), it should become clear why the simplicity of the proposed approach is a desirable feature for hyperparameter optimization in the NCD context. 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

Optimizing Novel Class Discovery with NCD Spectral Clustering and k-means

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk);
(2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK;
(3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia;
(4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro
Observation
Orbital Ephemeris
Radial Velocity Analysis
Spectral Analysis
Analysis of the Light Curve
Physical Properties
Asteroseismic Analysis
Discussion
Conclusion, Data Availability, Acknowledgments, and References
Appendix A: Ephemeris Determination
Appendix B: Iteratively Prewhitened Frequencies
Appendix C: Detected Tidally Perturbed Pulsations 5 SPECTRAL ANALYSIS 5.1 Atmospheric parameters Since we planned to apply the method of SPD to extract the individual spectra of the components, normalisation of the observed spectra was of critical importance. We used a different approach than that in Section 4, where we extracted RVs. Here, we used the dedicated code described in Kolbas et al. (2015). First, the blaze function of échelle orders was fitted with a high-order polynomial function. Then, the normalised échelle orders were merged. When overlapping regions of successive échelle orders are sufficiently long, the very ends were cut off because of their low S/N. Échelle orders containing broad Balmer lines, in which it is not possible to define the blaze function with enough precision, were treated in a special way. For these échelle orders, the blaze function was interpolated from adjacent orders. This produces more reliable normalisation in spectral regions with broad Balmer lines than the usual pipeline procedures. For recent applications of this approach, please see Pavlovski et al. (2018, 2023); Lester et al. (2019, 2022); Wang et al. (2020, 2023). Overall, a slight difference in line depth between the two components is seen. The more massive component has deeper lines and slightly faster rotation. The Balmer lines are broadened by Stark broadening, and generally are not sensitive to the rotational broadening. Thus, we first optimised portions of the disentangled spectra free of the Balmer lines, primarily to discern the vsini values. We then performed optimal fitting of disentangled spectra centred on the Hβ and Hα lines, with fixed vsini. The light ratio from the optimal fitting of the wings of Hβ and Hγ lines, spectral segments containing only metal lines, and the Mg I triplet at around 5180 Å, are 1.315±0.018, 1.304±0.025 and 1.278±0.033 (Table 3). All three values for the light ratio are consistent within their 1σ uncertainties, with the light ratio determined from the wings of Hβ line being the most precise of the three. Guo et al. (2016) determined atmospheric parameters from tomographically reconstructed spectra of the components. Their spectra cover the wavelength range 3930–4610 Å at a medium spectral resolution of R = 6000. The analysis by Guo et al. (2016) was similar to ours, but with an important difference that they fitted complete separated spectra, whilst we concentrate on the wings of Balmer lines. Our results for the Balmer lines corroborate their findings to within 1σ. We also find good agreement for the vsini values, which are also within 1σ for both components. Guo et al. (2016) obtained a mean light ratio 1.34±0.03 from spectra centred at 4275 Å – this is slightly larger than but still consistent with our own results. 5.2 Direct fitting for the light ratio We were initially unable to constrain the light ratio of the system from the photometric analysis alone (see Section 3), so estimated it using several independent methods. The first method was the TODCOR light ratio reported in Table 2, the second was the optimal fitting of the disentangled spectra (ODS), and a third method is developed here. We repeated the process for all spectral orders showing sufficient well-resolved lines in regions unaffected by tellurics from Earth’s atmosphere. To save computing time, only the two observations closest to positions of quadrature were used. This approach resulted 036 30 spectral orders showing adequate fits with well defined minima in the sum of the square residuals. Fig.7 shows the result of this method applied to order 66, which demonstrates the effectiveness of optimizing the normalization of the observed spectra in the fitting routine for an order where line blending is significant. The resulting values for ℓB/ℓA after applying this method to the selected orders are shown in Fig.8, where each result is the average of the result from each of the two observations used. This paper is available on arxiv under CC 4.0 license. Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Z. Jennings, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK (E-mail: z.jennings@keele.ac.uk); (2) J. Southworth, Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK; (3) K. Pavlovski, Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; (4) T. Van Reeth, Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium. Table of Links Abstract and Intro Observation Orbital Ephemeris Radial Velocity Analysis Spectral Analysis Analysis of the Light Curve Physical Properties Asteroseismic Analysis Discussion Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Abstract and Intro Abstract and Intro Observation Observation Orbital Ephemeris Orbital Ephemeris Radial Velocity Analysis Radial Velocity Analysis Spectral Analysis Spectral Analysis Analysis of the Light Curve Analysis of the Light Curve Physical Properties Physical Properties Asteroseismic Analysis Asteroseismic Analysis Discussion Discussion Conclusion, Data Availability, Acknowledgments, and References Conclusion, Data Availability, Acknowledgments, and References Appendix A: Ephemeris Determination Appendix A: Ephemeris Determination Appendix B: Iteratively Prewhitened Frequencies Appendix B: Iteratively Prewhitened Frequencies Appendix C: Detected Tidally Perturbed Pulsations Appendix C: Detected Tidally Perturbed Pulsations 5 SPECTRAL ANALYSIS 5.1 Atmospheric parameters Since we planned to apply the method of SPD to extract the individual spectra of the components, normalisation of the observed spectra was of critical importance. We used a different approach than that in Section 4, where we extracted RVs. Here, we used the dedicated code described in Kolbas et al. (2015). First, the blaze function of échelle orders was fitted with a high-order polynomial function. Then, the normalised échelle orders were merged. When overlapping regions of successive échelle orders are sufficiently long, the very ends were cut off because of their low S/N. Échelle orders containing broad Balmer lines, in which it is not possible to define the blaze function with enough precision, were treated in a special way. For these échelle orders, the blaze function was interpolated from adjacent orders. This produces more reliable normalisation in spectral regions with broad Balmer lines than the usual pipeline procedures. For recent applications of this approach, please see Pavlovski et al. (2018, 2023); Lester et al. (2019, 2022); Wang et al. (2020, 2023). Overall, a slight difference in line depth between the two components is seen. The more massive component has deeper lines and slightly faster rotation. The Balmer lines are broadened by Stark broadening, and generally are not sensitive to the rotational broadening. Thus, we first optimised portions of the disentangled spectra free of the Balmer lines, primarily to discern the v sin i values. We then performed optimal fitting of disentangled spectra centred on the Hβ and Hα lines, with fixed v sin i . v i v i The light ratio from the optimal fitting of the wings of Hβ and Hγ lines, spectral segments containing only metal lines, and the Mg I triplet at around 5180 Å, are 1.315±0.018, 1.304±0.025 and 1.278±0.033 (Table 3). All three values for the light ratio are consistent within their 1σ uncertainties, with the light ratio determined from the wings of Hβ line being the most precise of the three. Guo et al. (2016) determined atmospheric parameters from tomographically reconstructed spectra of the components. Their spectra cover the wavelength range 3930–4610 Å at a medium spectral resolution of R = 6000. The analysis by Guo et al. (2016) was similar to ours, but with an important difference that they fitted complete separated spectra, whilst we concentrate on the wings of Balmer lines. Our results for the Balmer lines corroborate their findings to within 1σ. We also find good agreement for the v sin i values, which are also within 1σ for both components. Guo et al. (2016) obtained a mean light ratio 1.34±0.03 from spectra centred at 4275 Å – this is slightly larger than but still consistent with our own results. v i 5.2 Direct fitting for the light ratio We were initially unable to constrain the light ratio of the system from the photometric analysis alone (see Section 3), so estimated it using several independent methods. The first method was the TODCOR light ratio reported in Table 2, the second was the optimal fitting of the disentangled spectra (ODS), and a third method is developed here. We repeated the process for all spectral orders showing sufficient well-resolved lines in regions unaffected by tellurics from Earth’s atmosphere. To save computing time, only the two observations closest to positions of quadrature were used. This approach resulted 036 30 spectral orders showing adequate fits with well defined minima in the sum of the square residuals. Fig.7 shows the result of this method applied to order 66, which demonstrates the effectiveness of optimizing the normalization of the observed spectra in the fitting routine for an order where line blending is significant. The resulting values for ℓB/ℓA after applying this method to the selected orders are shown in Fig.8, where each result is the average of the result from each of the two observations used.

Physical Properties of the Eclipsing Binary KIC 9851944: Spectral Analysis

RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. Contact Information:
Email: info@rtv.com
Number: +1 (565) 987-6543 RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. About RTV: Contact Information:
Email: info@rtv.com Number: +1 (565) 987-6543 info@rtv.com

Swish Returns: RTV's Musical Theater Spectacle Makes a Comeback

NEW YORK, New York, May 7th, 2024/Chainwire/-- Spectral is excited to announce its participation in Hugging Face's Expert Support Program. Spectral is collaborating with deep learning experts from Hugging Face to advance open-source models, datasets, and applications for the Onchain Agent Economy. How To Use Hugging Face Hugging Face plays a critical role across Spectral's organization. Through the Expert Support Program, Spectral regularly meets with the Hugging Face team to plan new use cases, strategize dataset construction, and develop training strategies. Earlier this year, Spectral launched Syntax, which is composed of a fine-tuned LLM orchestrator that routes queries between several tools, including search, Foundry, and an open-source model to generate Solidity code. Building Syntax required custom datasets for both finetuning and RAG, an original Solidity evaluation dataset written by elite smart contract developers, and experimentation across a wide range of open and closed-source models. Spectral's approach to dataset preparation, training, routing, and evaluation has drastically improved during this process, largely due to help from Hugging Face experts. Regular discussions on Spectral's approach mean that the company always receives impartial feedback on its decisions and can reevaluate its strategy as needed. This was important for both the finetuning and evaluation steps, which have now gone through several rounds of improvement thanks to the Hugging Face team. Spectral also relies on Hugging Face for inferences in production through dedicated Inference Endpoints. The fully managed infrastructure allows Spectral to iterate on new models quickly, easily update container and hardware configurations, autoscale with demand, and keep production costs low. Over the coming months, Spectral is excited to continue to open-source its work with onchain datasets and models that interact onchain through Hugging Face. Users can view Spectral's training dataset for the credit scoring challenge and follow its progress here. The Onchain Agent Economy Syntax is pioneering the accessible onchain Agent Economy, inviting users to select agents tailored for their specific tasks. Users can either interact with the foundational agent to generate solidity code or opt for one of the specialized agents, each adept in distinct tasks. For example, we recently launched Syntax MoonMaker, an agent that launches a memecoin project end to end and are soon launching Syntax- TestMachine, an agent that detects vulnerabilities in your smart contracts. Many such agents are currently in production, and upcoming releases of our product will allow users to create their own agents and monetize them on the Syntax network. This system relies on a vast suite of functions available to language models, open-source models for tasks like classification and image generation, fast and reliable blockchain infrastructure, and a trustless system to execute and verify each step. To solve these problems, we've begun developing the Inferchain, optimized to serve the rapidly growing demand for agents and verifiable inferences. Hugging Face Users Joining The Onchain AI Community The importance of open collaboration cannot be overstated. Like many other projects, Spectral would not exist without the work of open-source contributors. Spectral is greatly indebted to everyone who makes this work possible and believes this positive impact should be recognized. As part of this engagement, Spectral is focusing specifically on users powering the AI community. The top individual and small organization contributors on Hugging Face, measured by likes and downloads of their models and datasets, are eligible to register to claim a share of SPEC tokens starting the week of May 6th. Users can check eligibility by signing in with their Hugging Face account here. A snapshot of activity was taken last month, and eligible users will not need an existing wallet to claim. This airdrop is designed to recognize users for their critical work and enable new functionality for the next generation of AI tools. Criteria Spectral reviewed all individual contributors from organizations with less than 10 members that published models or datasets. From these, Spectral took a combined measure of activity from likes and downloads for both models and datasets. Then, Spectral identified the users with the most combined activity from both model and dataset publishers (11298 in both categories) which resulted in a final list of 20,004 unique huggingface users. Spectral is looking forward to engaging further with the open-source community and building the future of AI x web3! https://claims.spectrallabs.xyz/ About Spectral Spectral, a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Spectral's mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join Spectral in making this vision a reality and explore the potential of autonomous agents. For more information, visit https://spectrallabs.xyz. For more information, users can follow Spectral on Twitter and Discord. Contact Spectral Labs Team contact@spectral.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. NEW YORK, New York, May 7th, 2024/Chainwire/-- Spectral is excited to announce its participation in Hugging Face's Expert Support Program. Spectral is collaborating with deep learning experts from Hugging Face to advance open-source models, datasets, and applications for the Onchain Agent Economy. How To Use Hugging Face Hugging Face plays a critical role across Spectral's organization. Through the Expert Support Program, Spectral regularly meets with the Hugging Face team to plan new use cases, strategize dataset construction, and develop training strategies. Earlier this year, Spectral launched Syntax, which is composed of a fine-tuned LLM orchestrator that routes queries between several tools, including search, Foundry , and an open-source model to generate Solidity code. Foundry Foundry an open-source model an open-source model Building Syntax required custom datasets for both finetuning and RAG, an original Solidity evaluation dataset written by elite smart contract developers, and experimentation across a wide range of open and closed-source models. Spectral's approach to dataset preparation, training, routing, and evaluation has drastically improved during this process, largely due to help from Hugging Face experts. Regular discussions on Spectral's approach mean that the company always receives impartial feedback on its decisions and can reevaluate its strategy as needed. This was important for both the finetuning and evaluation steps, which have now gone through several rounds of improvement thanks to the Hugging Face team. Spectral also relies on Hugging Face for inferences in production through dedicated Inference Endpoints. The fully managed infrastructure allows Spectral to iterate on new models quickly, easily update container and hardware configurations, autoscale with demand, and keep production costs low. Over the coming months, Spectral is excited to continue to open-source its work with onchain datasets and models that interact onchain through Hugging Face. Users can view Spectral's training dataset for the credit scoring challenge and follow its progress here . here here The Onchain Agent Economy Syntax is pioneering the accessible onchain Agent Economy, inviting users to select agents tailored for their specific tasks. Users can either interact with the foundational agent to generate solidity code or opt for one of the specialized agents, each adept in distinct tasks. For example, we recently launched Syntax MoonMaker, an agent that launches a memecoin project end to end and are soon launching Syntax- TestMachine, an agent that detects vulnerabilities in your smart contracts. Many such agents are currently in production, and upcoming releases of our product will allow users to create their own agents and monetize them on the Syntax network. This system relies on a vast suite of functions available to language models, open-source models for tasks like classification and image generation, fast and reliable blockchain infrastructure, and a trustless system to execute and verify each step. To solve these problems, we've begun developing the Inferchain, optimized to serve the rapidly growing demand for agents and verifiable inferences. Hugging Face Users Joining The Onchain AI Community The importance of open collaboration cannot be overstated. Like many other projects, Spectral would not exist without the work of open-source contributors. Spectral is greatly indebted to everyone who makes this work possible and believes this positive impact should be recognized. As part of this engagement, Spectral is focusing specifically on users powering the AI community. The top individual and small organization contributors on Hugging Face, measured by likes and downloads of their models and datasets, are eligible to register to claim a share of SPEC tokens starting the week of May 6th. Users can check eligibility by signing in with their Hugging Face account here . here here A snapshot of activity was taken last month, and eligible users will not need an existing wallet to claim. This airdrop is designed to recognize users for their critical work and enable new functionality for the next generation of AI tools. Criteria Spectral reviewed all individual contributors from organizations with less than 10 members that published models or datasets. From these, Spectral took a combined measure of activity from likes and downloads for both models and datasets. Then, Spectral identified the users with the most combined activity from both model and dataset publishers (11298 in both categories) which resulted in a final list of 20,004 unique huggingface users. Spectral is looking forward to engaging further with the open-source community and building the future of AI x web3! https://claims.spectrallabs.xyz/ https://claims.spectrallabs.xyz/ https://claims.spectrallabs.xyz/ About Spectral Spectral , a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Spectral's mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Spectral Spectral Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join Spectral in making this vision a reality and explore the potential of autonomous agents. For more information, visit https://spectrallabs.xyz . For more information, users can follow Spectral on Twitter and Discord . https://spectrallabs.xyz Twitter Twitter Discord Discord Contact Spectral Labs Team contact@spectral.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Spectral Labs Joins Hugging Face’s ESP Program: Advancing The Onchain x Open-Source AI Community

স্পেকট্রাল ল্যাবগুলি আলিঙ্গন মুখের ইএসপি প্রোগ্রামে যোগ দেয়: অনচেইন এক্স ওপেন-সোর্স এআই সম্প্রদায়কে এগিয়ে নিয়ে যাওয়া

Spectral Labs nimmt am ESP-Programm von Hugging Face teil: Förderung der Onchain x Open-Source-KI-Community

Spectral Labs se une al programa ESP de Hugging Face: avanzando en la comunidad de IA de código abierto Onchain x

Spectral Labs rejoint le programme ESP de Hugging Face : faire progresser la communauté d'IA Onchain x Open-Source

स्पेक्ट्रल लैब्स हगिंग फेस के ईएसपी कार्यक्रम में शामिल हुई: ऑनचेन एक्स ओपन-सोर्स एआई समुदाय को आगे बढ़ाना

Spectral Labs が Hugging Face の ESP プログラムに参加: オンチェーン x オープンソース AI コミュニティの発展

Spectral Labs, Hugging Face의 ESP 프로그램에 합류: Onchain x 오픈 소스 AI 커뮤니티 발전

Spectral Labs junta-se ao programa ESP da Hugging Face: Avançando a comunidade Onchain x Open-Source AI

Spectral Labs присоединяется к программе ESP Hugging Face: развитие сообщества искусственного интеллекта с открытым исходным кодом Onchain x

Spectral Labs, Hugging Face'in ESP Programına Katılıyor: Onchain x Açık Kaynak Yapay Zeka Topluluğunu Geliştirme

Spectral Labs tham gia chương trình ESP của Hugging Face: Thúc đẩy cộng đồng AI mã nguồn mở Onchain x

Spectral Labs 加入 Hugging Face 的 ESP 计划：推进 Onchain x 开源 AI 社区

NEW YORK, New York, March 26th, 2024/Chainwire/--**Syntax takes natural language and converts it into Solidity code, enabling individuals and enterprises to ship production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Syntax marks the manifestation of the Agent Economy in Web3. Today, has launched Syntax, a new LLM enabling people to ship agents that automate onchain projects and tasks, thereby manifesting their Web3 product ideas. While still in beta, Syntax’s vision is to create an open onchain Agent Economy, where developers, individuals, and enterprises can for the first time select the agents they would want to delegate their Web3 tasks, projects, and product development to. These projects and products include production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Spectral Labs Scaling web3 engineering efforts and shipping new onchain products is limited by the number of solidity developers, as well as enterprises’ ability to test and ship smart contract code in a fast and effective way without burning through engineering resources. Spectral has set out to solve this with its new LLM, Syntax, a development environment designed to manifest Web3 product ideas by converting natural language into Solidity code that can be deployed instantly onchain. For professional developers, Syntax can accelerate and optimize bandwidth by writing large libraries and components. Syntax provides an Agentic experience, where users tell Syntax what they want to build, and Syntax writes and deploys the code onchain at the click of a button. Spectral defines an Onchain Agent as a set of instructions and code capable of deploying itself onchain and possessing a dedicated provisioned wallet for doing so. Loosely similar to custom instructions in ChatGPT, Syntax Agents also have their own identity and can autonomously figure out ways to interact with deployment infrastructure. With Syntax, users can compile, debug, and deploy AI-generated solidity code. Syntax gives users and the industry the ability to quickly test, ship and scale new products, as well as determining the usability and functional relevance of the code generated while also giving them control to quickly edit code snippets. Initially, Spectral’s Onchain Agents will be curated and built by Spectral, but soon every user will be able to create their own Agent and monetize it in future Syntax releases. "At Spectral, we're pioneering a future where autonomous Agents redefine what's possible onchain. With Spectral Syntax, we empower these agents to operate independently, making it simpler for everyone to bring their blockchain ideas to life." Sishir Varghese, Founder. What truly sets Syntax apart is its capability to deploy code directly onchain, executing deployments through Foundry by writing instructions that Foundry can process, including code compilation, onchain deployment, and signing transactions. Syntax is pioneering an accessible onchain Agent Economy, inviting users to select agents tailored for their specific Web3 tasks. Users can either interact with the foundational agent or opt for one of the specialized agents, each adept in distinct tasks and operations—like the Code Forker assistant, ideal for those aiming to fork and customize a codebase. Additionally, users can discern an agent's expertise and trustworthiness by reviewing its category listing and the popularity reflected through its user engagement. "Syntax by Spectral is at the forefront of merging AI with web3, pioneering the onchain agent economy and making it easier and more accessible for users to create their own blockchain products," said Josh Rosenthal, Partner, Polychain Syntax produces accurate and gas-optimized Solidity code for a variety of prompts ranging from low to medium complexity, and it outperforms baseline models on Spectral’s Solidity evaluation dataset. As Spectral’s modeling efforts continue to evolve, the company expects Syntax to deliver optimized code for increasingly complex problems. These Solidity models are explicitly designed to assist developers in writing optimized, modern, well-tested, and secure code. About Spectral , a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Our mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. Spectral With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join us in making this vision a reality and explore the potential of autonomous agents with Spectral. For more information, visit /. For more information, users can follow Spectral on and . https://spectrallabs.xyz Twitter Discord Contact Spectral Labs Spectral info@spectrallabs.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

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