The AMA allows Veterans to decide which method of claim decision review is best for them, depending on the circumstances of their claim.

VA celebrates five years of appeals modernization - VA News

Updated COVID vaccines are available free of charge to Veterans receiving care at VA .

VA offering updated COVID-19 vaccines - VA News

VA&#x27;s Gerofit program, designed for Veterans aged 65+, promotes physical activity that helps to improve health and mental well-being.

Gerofit keeps older Veterans active through personalized wellness - VA News

VA has announced an update to the VA Schedule for Rating Disabilities (VASRD) specifically pertaining to digestive conditions.

VA updates Disability Rating Schedule for digestive system - VA News

This post contains links to jobs for the week of March 18, 2024. Each week, we’ll continue to post relevant and timely listings as we receive them.

Hiring Veterans: Jobs of the week for March 18, 2024 - VA News

Veteran calls device designed to treat sleep apnea “a life changer.”

Sleep apnea device improves Veteran’s health and sleep - VA News

If you’re ready to start a career serving Veterans, VA will help you with your tuition costs thanks to these two amazing scholarship opportunities.

Get VA to cover the cost of your degree with these scholarship programs - VA News

Ever wanted to plan a vacation without having to worry about flight options, places to stay, or what to eat—all while saving money? Artificial intelligence (A.I.) is turning this wish into reality, sparking a revolution across the travel sector through custom experiences for travelers. From trip itinerary planning to handling traveler details, A.I. now can impact every step of the journey. McKinsey’s survey estimates that generative AI (genAI) could unlock between $2 trillion to $4 trillion in annual value across various domains, and travel is no exception. A.I. is empowering consumers to make informed decisions through predictive analytics, reducing the guesswork often linked to planning trips. Moreover, according to a recent report by Statista, 40% of US adults are already using AI-powered virtual travel agents for trip planning in 2024. However, as AI becomes a bigger part of how we travel, it also raises significant concerns about consumer data privacy and whether we're losing the personal touch in our journeys. A.I.-Powered Travel Planning: The New Norm? In travel, A.I.'s impact seems twofold. On the consumer side, A.I. is reshaping ways for trip planning, a fragmented and time-consuming process for travelers. Travel booking platforms including Agoda are already harnessing A.I.-powered machine learning to recommend and optimize booking prices for flights, and lodging options to its customers. Historically, personalization in travel has been a challenging issue primarily because of the low purchase frequency and customer data spread being across various platforms. Each trip often carries a different motivation, making it difficult for traditional systems to understand and predict customer preferences based solely on previous clicks. This is where A.I. models play a crucial role. “The real challenge lies in efficiently gathering this context from users. Relying solely on traditional interfaces to extract such detailed information often led to high user expectations not being met by the website’s recommendations,” Amirali Mohajer, CEO and co-founder of A.I.-powered travel platform hostAI, told me. “Today’s AI-powered language models are adept at capturing context and interpreting text. They can significantly enhance travel personalization by understanding what a traveler seeks in their next adventure.” On the other hand, A.I. is providing travel brands with a significant opportunity to enhance their online presence and direct engagement with consumers. Dynamic pricing recommendations by A.I. are helping hotel owners maximize revenue and occupancy rates. “By automating personalization, even smaller accommodation providers can now effectively communicate their offerings, potentially shifting the industry towards a new standard of direct distribution. This innovation could fundamentally alter how travel services are marketed and sold, making them more accessible and efficient for both providers and consumers,” Mohajer told me. Can A.I. Replace Human Travel Agents? Achieving a level of A.I. that offers the warm, personalized touch of a human travel agent is no small feat and involves overcoming persistent technical challenges within the travel sector. For instance, Air Canada's chatbot misled a traveler by promising a discount that wasn’t available. The chatbot assured the consumer that he could book a full-fare ticket for his grandmother’s funeral and apply for a bereavement fare afterward. However, when the consumer sought the discount, Air Canada informed him that the chatbot's information was incorrect. This kind of error, where AI tools provide inaccurate or misleading information, is known as "AI hallucination." Despite such challenges, many major travel companies are adopting AI technology. Last year, Lufthansa Group launched "Swifty," a genAI-driven business travel assistant designed to streamline corporate trip planning and booking processes. Powered by OpenAI’s GPT-4 language model, Swifty can overhaul travel experiences for consumers — from planning and payment to solving consumer queries in real-time, all within a single chat interface. Data privacy remains another significant concern across various domains, not just in travel but in broader A.I. applications as well. Recently, Australian travel agency Inspiring Vacations was accused of exposing sensitive customer data by accidentally leaving its database publicly accessible. Totaling 26.8 gigabytes, the database included potentially sensitive information such as passport images, travel visa certificates, itineraries, and ticket files. "AI systems frequently encounter hurdles with reasoning and taking effective actions because of hallucination. This limitation might pave the way for a revival of traditional travel agents, but with a new twist: they'll be enhanced by AI," Mohajer told me. “For technology deployments to succeed, particularly those aiming to foster intimate user relationships like AI chatbots, building and maintaining trust is crucial. Users need to feel confident that their data is handled responsibly.” The Future of AI in Travel: What's Next? AI is no longer just a futuristic concept in the travel industry—it's a present-day reality transforming how consumers plan and experience travel. As technology advances, Mohajer believes that A.I.'s role in the travel sector will likely evolve into a continuous, personalized travel companion. This companion would seek out the best opportunities for future trips and enhance the in-destination experience by eliminating the common frustration of navigating a new place. “Imagine an A.I. system that finds deals and recommends destinations tailored to individual preferences, possibly even lesser-known locales that might better match your interests. This shift could mitigate over-tourism by diverting travelers to less crowded areas and make travel more affordable and enjoyable overall," he added. “The vision is to create a seamless, personalized experience that eliminates the need for endless searches and planning across multiple platforms—a challenge that remains open yet highly pursued by industry giants and startups alike" Ever wanted to plan a vacation without having to worry about flight options, places to stay, or what to eat—all while saving money? Artificial intelligence (A.I.) is turning this wish into reality, sparking a revolution across the travel sector through custom experiences for travelers. From trip itinerary planning to handling traveler details, A.I. now can impact every step of the journey. McKinsey ’s survey estimates that generative AI (genAI) could unlock between $2 trillion to $4 trillion in annual value across various domains, and travel is no exception. A.I. is empowering consumers to make informed decisions through predictive analytics, reducing the guesswork often linked to planning trips. Moreover, according to a recent report by Statista , 40% of US adults are already using AI-powered virtual travel agents for trip planning in 2024. McKinsey McKinsey Statista Statista However, as AI becomes a bigger part of how we travel, it also raises significant concerns about consumer data privacy and whether we're losing the personal touch in our journeys. A.I.-Powered Travel Planning: The New Norm? In travel, A.I.'s impact seems twofold. On the consumer side, A.I. is reshaping ways for trip planning, a fragmented and time-consuming process for travelers. Travel booking platforms including Agoda are already harnessing A.I.-powered machine learning to recommend and optimize booking prices for flights, and lodging options to its customers. Historically, personalization in travel has been a challenging issue primarily because of the low purchase frequency and customer data spread being across various platforms. Each trip often carries a different motivation, making it difficult for traditional systems to understand and predict customer preferences based solely on previous clicks. This is where A.I. models play a crucial role. “The real challenge lies in efficiently gathering this context from users. Relying solely on traditional interfaces to extract such detailed information often led to high user expectations not being met by the website’s recommendations,” Amirali Mohajer , CEO and co-founder of A.I.-powered travel platform hostAI, told me. “Today’s AI-powered language models are adept at capturing context and interpreting text. They can significantly enhance travel personalization by understanding what a traveler seeks in their next adventure.” Amirali Mohajer Amirali Mohajer On the other hand, A.I. is providing travel brands with a significant opportunity to enhance their online presence and direct engagement with consumers. Dynamic pricing recommendations by A.I. are helping hotel owners maximize revenue and occupancy rates. “By automating personalization, even smaller accommodation providers can now effectively communicate their offerings, potentially shifting the industry towards a new standard of direct distribution. This innovation could fundamentally alter how travel services are marketed and sold, making them more accessible and efficient for both providers and consumers,” Mohajer told me. Can A.I. Replace Human Travel Agents? Achieving a level of A.I. that offers the warm, personalized touch of a human travel agent is no small feat and involves overcoming persistent technical challenges within the travel sector. For instance, Air Canada's chatbot misled a traveler by promising a discount that wasn’t available. The chatbot assured the consumer that he could book a full-fare ticket for his grandmother’s funeral and apply for a bereavement fare afterward. However, when the consumer sought the discount, Air Canada informed him that the chatbot's information was incorrect. This kind of error, where AI tools provide inaccurate or misleading information, is known as "AI hallucination." Air Canada's chatbot Air Canada's chatbot Despite such challenges, many major travel companies are adopting AI technology. Last year, Lufthansa Group launched " Swifty ," a genAI-driven business travel assistant designed to streamline corporate trip planning and booking processes. Powered by OpenAI’s GPT-4 language model, Swifty can overhaul travel experiences for consumers — from planning and payment to solving consumer queries in real-time, all within a single chat interface. Swifty Swifty Data privacy remains another significant concern across various domains, not just in travel but in broader A.I. applications as well. Recently, Australian travel agency Inspiring Vacations was accused of exposing sensitive customer data by accidentally leaving its database publicly accessible. Totaling 26.8 gigabytes, the database included potentially sensitive information such as passport images, travel visa certificates, itineraries, and ticket files. Inspiring Vacations Inspiring Vacations "AI systems frequently encounter hurdles with reasoning and taking effective actions because of hallucination. This limitation might pave the way for a revival of traditional travel agents, but with a new twist: they'll be enhanced by AI," Mohajer told me. “For technology deployments to succeed, particularly those aiming to foster intimate user relationships like AI chatbots, building and maintaining trust is crucial. Users need to feel confident that their data is handled responsibly.” The Future of AI in Travel: What's Next? AI is no longer just a futuristic concept in the travel industry—it's a present-day reality transforming how consumers plan and experience travel. As technology advances, Mohajer believes that A.I.'s role in the travel sector will likely evolve into a continuous, personalized travel companion. This companion would seek out the best opportunities for future trips and enhance the in-destination experience by eliminating the common frustration of navigating a new place. “Imagine an A.I. system that finds deals and recommends destinations tailored to individual preferences, possibly even lesser-known locales that might better match your interests. This shift could mitigate over-tourism by diverting travelers to less crowded areas and make travel more affordable and enjoyable overall," he added. “The vision is to create a seamless, personalized experience that eliminates the need for endless searches and planning across multiple platforms—a challenge that remains open yet highly pursued by industry giants and startups alike"

Will an AI Help Plan Your Next Vacation?

GEORGETOWN, Cayman Islands, December 16th, 2024/Chainwire/--During the testnet phase, over 1.3m users contributed more than 6.5m data points to train user-owned AI models. Vana, the pioneering network for user-owned data, today announced the launch of its mainnet and native $VANA token. The launch marks a significant milestone in breaking through the data wall that’s limiting AI development while preserving privacy and giving users a stake in the value their data generates. Developed by Open Data Labs — a San Francisco-based research and technology company born out of the MIT Media Lab — the Vana network is an EVM-compatible blockchain that enables users to maintain ownership and control over their personal data while allowing that data to be used for AI model training through privacy-preserving technologies. The mainnet launch follows Vana's successful testnet phase, which saw over 1.3 million users contributing over 6.5m data points to DataDAOs, processing approximately 1.7 million transactions daily. The testnet demonstrated that programmable data ownership is not just possible, but critical at this point in AI development, says Anna Kazlauskas, CEO of Open Data Labs and inventor of Vana: "Today's mainnet launch marks a fundamental shift in how data is owned and monetized in the AI era. Users have always legally owned their data, but platforms have captured all the economic value — and most users don't even realize their data is legally theirs! “Vana lays the groundwork for a new kind of data economy - one where users can benefit from the AI models they help create, and where developers can finally access the cross-platform datasets needed to build truly powerful AI. Property rights were the foundation that enabled modern economies to flourish - and in today's digital economy, programmable data rights are the foundation for the next generation of AI.” The key features of the Vana mainnet will include: Trustless Validation through secure data ingress and trusted execution nodes
Granular Permissioning allowing users complete control over their data usage
Onchain Data Provenance tracking how contributed data is applied
Data Liquidity Pools (DLPs) enabling collective data pooling, governance and monetization via DataDAOs
Proof of Contribution mechanism ensuring fair rewards for data quality
Staking $VANA to DataDAOs through Data Hub With the network's proof-of-contribution system, users can earn by contributing their data to DataDAOs - receiving dataset-specific tokens that grant governance rights while maintaining control over how their data are used. These data tokens can be traded for $VANA, the network's native currency. The top DataDAOs also receive DataDAO rewards. Beyond data contributions, users can earn additional tokens by running nodes, validating transactions, and staking to DataDAOs. With a total supply of 120 million tokens, $VANA will serve as the foundation for the network's economic model, enabling: Governance over network parameters and data usage
Staking to secure the network and validate data contributions
Incentivization of high-quality data submissions from data contributors
Revenue Sharing from AI model development and data usage Among the DataDAOs building on Vana are DNA DAO, addressing privacy concerns around genetic data ownership, and an expansion of the successful Reddit Data DAO, which has already demonstrated the potential of community-owned data pools. Read more about DataDAOs on Vana here. The mainnet launch comes at a crucial time when AI development faces increasing data scarcity, while users seek greater control over their digital footprint. Vana's solution enables individuals to participate in and benefit from the AI economy while maintaining sovereignty over their data. As AI hits a data wall and more platforms monetize user data without sharing any upside with users, users deserve to own a stake in the future of AI, says Art Abal, CEO of Vana Foundation: “The current way we buy and sell private data in Web2 is broken. Our data is extracted and held for ransom by a few platforms and data-brokers. They decide how much our data is worth, and the technology it creates. No more. This is just the beginning of a future where we can decide our data’s worth and decide what technology it creates. This is the data revolution!” Users can start staking $VANA on Data Hub here. For more information, users can visit www.vana.org, read our docs, follow us on X, launch a new DataDAO. Media Contact: media@vanafoundation.org About Vana Vana is the first decentralized network for user-owned data, unlocking data as a new digital asset class. The Vana network consists of an EVM-compatible blockchain, secure personal server environment, and set of native contracts designed for the trustless and secure exchange of user-owned data via DataDAOs. The network empowers users to maintain control over their data while participating in the growing AI economy by pooling their data with others and earning rewards for their contributions. Media Kit | Vana Docs | X | Discord About Vana Foundation Vana Foundation is a non-profit entity tasked with ensuring the sustainability and growth of the Vana ecosystem. About Open Data Labs Open Data Labs is an independent research company focused on technology to accelerate user-owned data. Open Data Labs created the Vana protocol and provides ongoing core developer services to the Vana Foundation. Contact Nick Vivion Vana Foundation media@vanafoundation.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. GEORGETOWN, Cayman Islands, December 16th, 2024/Chainwire/--During the testnet phase, over 1.3m users contributed more than 6.5m data points to train user-owned AI models. Vana , the pioneering network for user-owned data, today announced the launch of its mainnet and native $VANA token. Vana Vana The launch marks a significant milestone in breaking through the data wall that’s limiting AI development while preserving privacy and giving users a stake in the value their data generates. Developed by Open Data Labs — a San Francisco-based research and technology company born out of the MIT Media Lab — the Vana network is an EVM-compatible blockchain that enables users to maintain ownership and control over their personal data while allowing that data to be used for AI model training through privacy-preserving technologies. Open Data Labs Open Data Labs The mainnet launch follows Vana's successful testnet phase, which saw over 1.3 million users contributing over 6.5m data points to DataDAOs, processing approximately 1.7 million transactions daily. The testnet demonstrated that programmable data ownership is not just possible, but critical at this point in AI development, says Anna Kazlauskas, CEO of Open Data Labs and inventor of Vana: Open Data Labs Open Data Labs "Today's mainnet launch marks a fundamental shift in how data is owned and monetized in the AI era. Users have always legally owned their data, but platforms have captured all the economic value — and most users don't even realize their data is legally theirs! "Today's mainnet launch marks a fundamental shift in how data is owned and monetized in the AI era. Users have always legally owned their data, but platforms have captured all the economic value — and most users don't even realize their data is legally theirs! “Vana lays the groundwork for a new kind of data economy - one where users can benefit from the AI models they help create, and where developers can finally access the cross-platform datasets needed to build truly powerful AI. Property rights were the foundation that enabled modern economies to flourish - and in today's digital economy, programmable data rights are the foundation for the next generation of AI.” “Vana lays the groundwork for a new kind of data economy - one where users can benefit from the AI models they help create, and where developers can finally access the cross-platform datasets needed to build truly powerful AI. Property rights were the foundation that enabled modern economies to flourish - and in today's digital economy, programmable data rights are the foundation for the next generation of AI.” The key features of the Vana mainnet will include: Trustless Validation through secure data ingress and trusted execution nodes Granular Permissioning allowing users complete control over their data usage Onchain Data Provenance tracking how contributed data is applied Data Liquidity Pools (DLPs) enabling collective data pooling, governance and monetization via DataDAOs Proof of Contribution mechanism ensuring fair rewards for data quality Staking $VANA to DataDAOs through Data Hub Trustless Validation through secure data ingress and trusted execution nodes Granular Permissioning allowing users complete control over their data usage Onchain Data Provenance tracking how contributed data is applied Data Liquidity Pools (DLPs) enabling collective data pooling, governance and monetization via DataDAOs Proof of Contribution mechanism ensuring fair rewards for data quality Staking $VANA to DataDAOs through Data Hub Data Hub Data Hub With the network's proof-of-contribution system, users can earn by contributing their data to DataDAOs - receiving dataset-specific tokens that grant governance rights while maintaining control over how their data are used. These data tokens can be traded for $VANA, the network's native currency. The top DataDAOs also receive DataDAO rewards . Beyond data contributions, users can earn additional tokens by running nodes, validating transactions, and staking to DataDAOs. DataDAO rewards DataDAO rewards With a total supply of 120 million tokens, $VANA will serve as the foundation for the network's economic model, enabling: Governance over network parameters and data usage Staking to secure the network and validate data contributions Incentivization of high-quality data submissions from data contributors Revenue Sharing from AI model development and data usage Governance over network parameters and data usage Staking to secure the network and validate data contributions Incentivization of high-quality data submissions from data contributors Revenue Sharing from AI model development and data usage Among the DataDAOs building on Vana are DNA DAO, addressing privacy concerns around genetic data ownership, and an expansion of the successful Reddit Data DAO, which has already demonstrated the potential of community-owned data pools. Read more about DataDAOs on Vana here . here here The mainnet launch comes at a crucial time when AI development faces increasing data scarcity, while users seek greater control over their digital footprint. Vana's solution enables individuals to participate in and benefit from the AI economy while maintaining sovereignty over their data. As AI hits a data wall and more platforms monetize user data without sharing any upside with users, users deserve to own a stake in the future of AI, says Art Abal, CEO of Vana Foundation: “The current way we buy and sell private data in Web2 is broken. Our data is extracted and held for ransom by a few platforms and data-brokers. They decide how much our data is worth, and the technology it creates. No more. This is just the beginning of a future where we can decide our data’s worth and decide what technology it creates. This is the data revolution!” “The current way we buy and sell private data in Web2 is broken. Our data is extracted and held for ransom by a few platforms and data-brokers. They decide how much our data is worth, and the technology it creates. No more. This is just the beginning of a future where we can decide our data’s worth and decide what technology it creates. This is the data revolution!” Users can start staking $VANA on Data Hub here . For more information, users can visit www.vana.org , read our docs , follow us on X , launch a new DataDAO . here here www.vana.org www.vana.org read our docs read our docs follow us on X follow us on X launch a new DataDAO launch a new DataDAO Media Contact: media@vanafoundation.org media@vanafoundation.org media@vanafoundation.org About Vana Vana is the first decentralized network for user-owned data, unlocking data as a new digital asset class. The Vana network consists of an EVM-compatible blockchain, secure personal server environment, and set of native contracts designed for the trustless and secure exchange of user-owned data via DataDAOs. Vana Vana The network empowers users to maintain control over their data while participating in the growing AI economy by pooling their data with others and earning rewards for their contributions. Media Kit | Vana Docs | X | Discord Media Kit Media Kit Vana Docs Vana Docs X X Discord Discord About Vana Foundation Vana Foundation is a non-profit entity tasked with ensuring the sustainability and growth of the Vana ecosystem. Vana Foundation Vana Foundation About Open Data Labs Open Data Labs is an independent research company focused on technology to accelerate user-owned data. Open Data Labs created the Vana protocol and provides ongoing core developer services to the Vana Foundation. Open Data Labs Open Data Labs Contact Nick Vivion Vana Foundation media@vanafoundation.org 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

Vana Mainnet Goes Live With $VANA To Power Data As a New Asset Class In Global AI Economy

Vana Mainnet går live med $VANA for at forsyne data som en ny aktivklasse i den globale AI-økonomi

Vana Mainnet se pone en marcha con $VANA para impulsar los datos como una nueva clase de activo en la economía global de la IA

Vana Mainnet ale ak $VANA pou pouvwa done kòm yon nouvo klas byen nan ekonomi AI mondyal

Vana メインネットが $VANA で稼働開始、グローバル AI 経済における新たな資産クラスとしてデータを強化

Vana Mainnet მუშაობს $VANA-ით, რათა უზრუნველყოს მონაცემები, როგორც ახალი აქტივის კლასი გლობალური AI ეკონომიკაში

Vana Mainnet sāk dzīvot ar $VANA, lai nodrošinātu datus kā jaunu aktīvu klasi globālajā AI ekonomikā

وانا مینیټ په نړیوال AI اقتصاد کې د نوي شتمني ټولګي په توګه د ډیټا ځواک کولو لپاره د $ VANA سره ژوند کوي

Global AI Economy හි නව වත්කම් පන්තියක් ලෙස දත්ත බල ගැන්වීම සඳහා Vana Mainnet $VANA සමඟ සජීවීව යයි

உலகளாவிய AI பொருளாதாரத்தில் புதிய சொத்து வகுப்பாக டேட்டாவை பவர் செய்ய $VANA உடன் Vana Mainnet செல்கிறது

ቫና ማይኔት ኣብ ዓለማዊ ቁጠባ ኤኣይ ከም ሓድሽ ንብረት ደርቢ ንዳታ ሓይሊ ንምሃብ ምስ $VANA ብቐጥታ ይኸይድ

Vana Mainnet Goes Live With $VANA To Power Data Bilang Bagong Asset Class Sa Global AI Economy

UVana Mainnet Uhamba kunye ne-$ VANA ukuya kwiDatha yaMandla njengeNgcaciso yeAsethi eNtsha kuQoqosho lwe-AI yeHlabathi.

SEYCHELLES, Seychelles, December 16th, 2024/Chainwire/--On November 27, 2024, BYDFi announced its official membership in South Korea’s CODE VASP Alliance. The partnership includes successful integration with the Travel Rule compliance solution, ensuring adherence to South Korea’s regulatory standards for crypto transactions. CODE formally welcomed BYDFi's participation at the same time as a post. This important moment reflects BYDFi's great attention to compliance and determined action and marks the crypto industry's gradual progress toward a more transparent, secure, and trustworthy future. CODE VASP: A Core Force for Compliance in Korea The CODE VASP, founded by Korea's top three crypto exchanges, Bithumb, Coinone, and Korbit, is Korea's only industry platform focused on “Travel Rules” compliance. The Alliance aims to provide technical support to Virtual Asset Service Providers (VASPs) to fully comply with the Global Travel Rules requirements set by the Financial Action Task Force (FATF), thereby meeting stringent Anti-Money Laundering (AML) and Counter-Terrorist Financing (CFT) standards. Integrating the compliance services of VerifyVASP, BYDFi moved in step with strict regulations set by South Korea under the Act on Reporting and Using Specific Financial Transaction Information. The move will improve safety and transparency for cross-platform asset transfers and establish BYDFi as a secure and compliant platform. BYDFi Official Insights: Why Joining the CODE Alliance Matters Michael, Co-founder of BYDFi, emphasized the importance of this partnership: “After a year of dedicated effort, we are thrilled to announce our successful integration with CODE VASP’s compliance solution. This milestone ensures that BYDFi fully complies with South Korea’s latest AML regulations, marking significant progress in our global compliance strategy. This collaboration not only strengthens our platform but also enhances user security, underscoring our commitment to delivering a world-class crypto trading experience.” This is an important milestone for BYDFi in its proactive approach toward compliance and innovation. The exchange is going to provide a secure and transparent home for trading with a view to cater to the demands of the South Korean market. About BYDFi Forbes-recognized among the top 10 global crypto exchanges, BYDFi offers access to over 600 tokens and supports leverage trading up to 1:200x. The integration with popular payment providers like Banxa, Transak, and Mercuryo facilitates buying cryptocurrencies, making it seamless and cost-effective for users of all backgrounds to purchase cryptocurrencies. The "BYDFi Copy Trading" function, soon to be launched, enables users to follow and reproduce the strategy of a top trader with just one click in order to optimize investments for maximum results. Continuing to develop innovative functions and always upholding the user-oriented philosophy, BYDFi is reaching the front line among the global leaders of safe, reliable, and intelligent crypto trading solutions. BYDFi Contacts For inquiries and support, users can reach BYDFi via the following: Website: https://www.bydfi.com Support Email: CS@bydfi.com Business Partnerships: BD@bydfi.com Media Inquiries: media@bydfi.com To stay connected with BYDFi through social media platforms: Twitter( X )| LinkedIn| Facebook | Telegram| YouTube Contact Senior Media Director Chloe BYDFi Fintech LTD chloe@bydfi.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SEYCHELLES, Seychelles, December 16th, 2024/Chainwire/--On November 27, 2024, BYDFi announced its official membership in South Korea’s CODE VASP Alliance. The partnership includes successful integration with the Travel Rule compliance solution, ensuring adherence to South Korea’s regulatory standards for crypto transactions. CODE formally welcomed BYDFi's participation at the same time as a post . This important moment reflects BYDFi's great attention to compliance and determined action and marks the crypto industry's gradual progress toward a more transparent, secure, and trustworthy future. as a post as a post CODE VASP: A Core Force for Compliance in Korea The CODE VASP , founded by Korea's top three crypto exchanges, Bithumb, Coinone, and Korbit, is Korea's only industry platform focused on “Travel Rules” compliance. CODE VASP CODE VASP The Alliance aims to provide technical support to Virtual Asset Service Providers (VASPs) to fully comply with the Global Travel Rules requirements set by the Financial Action Task Force (FATF), thereby meeting stringent Anti-Money Laundering (AML) and Counter-Terrorist Financing (CFT) standards. Integrating the compliance services of VerifyVASP , BYDFi moved in step with strict regulations set by South Korea under the Act on Reporting and Using Specific Financial Transaction Information. The move will improve safety and transparency for cross-platform asset transfers and establish BYDFi as a secure and compliant platform. VerifyVASP VerifyVASP BYDFi Official Insights: Why Joining the CODE Alliance Matters Michael, Co-founder of BYDFi, emphasized the importance of this partnership: “After a year of dedicated effort, we are thrilled to announce our successful integration with CODE VASP’s compliance solution. This milestone ensures that BYDFi fully complies with South Korea’s latest AML regulations, marking significant progress in our global compliance strategy. This collaboration not only strengthens our platform but also enhances user security, underscoring our commitment to delivering a world-class crypto trading experience.” Michael, Co-founder of BYDFi, emphasized the importance of this partnership: “After a year of dedicated effort, we are thrilled to announce our successful integration with CODE VASP’s compliance solution. This milestone ensures that BYDFi fully complies with South Korea’s latest AML regulations, marking significant progress in our global compliance strategy. This collaboration not only strengthens our platform but also enhances user security, underscoring our commitment to delivering a world-class crypto trading experience.” This is an important milestone for BYDFi in its proactive approach toward compliance and innovation. The exchange is going to provide a secure and transparent home for trading with a view to cater to the demands of the South Korean market. About BYDFi Forbes-recognized among the top 10 global crypto exchanges , BYDFi offers access to over 600 tokens and supports leverage trading up to 1:200x. The integration with popular payment providers like Banxa, Transak, and Mercuryo facilitates buying cryptocurrencies, making it seamless and cost-effective for users of all backgrounds to purchase cryptocurrencies. the top 10 global crypto exchanges the top 10 global crypto exchanges cost-effective cost-effective The " BYDFi Copy Trading " function, soon to be launched, enables users to follow and reproduce the strategy of a top trader with just one click in order to optimize investments for maximum results. BYDFi Copy Trading BYDFi Copy Trading Continuing to develop innovative functions and always upholding the user-oriented philosophy, BYDFi is reaching the front line among the global leaders of safe, reliable, and intelligent crypto trading solutions. BYDFi Contacts For inquiries and support, users can reach BYDFi via the following: Website: https://www.bydfi.com https://www.bydfi.com https://www.bydfi.com Support Email: CS@bydfi.com CS@bydfi.com CS@bydfi.com Business Partnerships: BD@bydfi.com BD@bydfi.com BD@bydfi.com Media Inquiries: media@bydfi.com media@bydfi.com media@bydfi.com To stay connected with BYDFi through social media platforms: Twitter( X ) | LinkedIn | Facebook | Telegram | YouTube Twitter( X ) Twitter( X ) LinkedIn LinkedIn Facebook Facebook Telegram Telegram YouTube YouTube Contact Senior Media Director Chloe BYDFi Fintech LTD chloe@bydfi.com 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

BYDFi Joins CODE VASP, Advancing Regulatory Efforts In Korea

BYDFi sluit aan by CODE VASP, wat regulatoriese pogings in Korea bevorder

BYDFi далучаецца да CODE VASP, прасоўваючы намаганні па рэгуляванні ў Карэі

BYDFi s'uneix a CODE VASP, avançant en els esforços reguladors a Corea

BYDFi se une a CODE VASP y avanza en los esfuerzos regulatorios en Corea

BYDFi מצטרף ל-CODE VASP, מקדם מאמצי רגולציה בקוריאה

BYDFi si unisce a CODE VASP, promuovendo gli sforzi normativi in Corea

BYDFiがCODE VASPに参加、韓国での規制強化の取り組みを前進

BYDFi dia manatevin-daharana amin'ny CODE VASP, mampandroso ny ezaka ara-dalàna any Korea

BYDFi bashkohet me CODE VASP, duke avancuar përpjekjet rregullatore në Kore

BYDFi ба CODE VASP, пешбурди кӯшишҳои танзим дар Корея ҳамроҳ мешавад

BYDFi Koreyada tartibga solish bo'yicha sa'y-harakatlarni rivojlantirish CODE VASPga qo'shildi

I-BYDFi Ijoyina IKHODI VASP, Ithuthukisa Imizamo Yokulawula E-Korea

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution;
(2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion and References A Discussion: Selection Mechanism B Related Work C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results 3.6 Additional Model Details Real vs. Complex. Most prior SSMs use complex numbers in their state ℎ, which is necessary for strong performance on many tasks (Gu, Goel, and Ré 2022). However, it has been empirically observed that completely real-valued SSMs seem to work fine, and possibly even better, in some settings (Ma et al. 2023). We use real values as the default, which work well for all but one of our tasks; we hypothesize that the complex-real tradeoff is related to the continuous-discrete spectrum in data modalities, where complex numbers are helpful for continuous modalities (e.g. audio, video) but not discrete (e.g. text, DNA). Initialization. Most prior SSMs also suggest special initializations, particularly in the complex-valued case, which can help in several settings such as low-data regimes. Our default initialization for the complex case is S4D-Lin and for the real case is S4D-Real (Gu, Gupta, et al. 2022), which is based on the HIPPO theory (Gu, Dao, et al. 2020). These define the n-th element of A as −1∕2 + ni and −(n + 1) respectively. However, we expect many initializations to work fine, particularly in the large-data and real-valued SSM regimes; some ablations are considered in Section 4.6. Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion and References 6 Conclusion and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work B Related Work C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results E Experimental Details and Additional Results 3.6 Additional Model Details Real vs. Complex. Most prior SSMs use complex numbers in their state ℎ, which is necessary for strong performance on many tasks (Gu, Goel, and Ré 2022). However, it has been empirically observed that completely real-valued SSMs seem to work fine, and possibly even better, in some settings (Ma et al. 2023). We use real values as the default, which work well for all but one of our tasks; we hypothesize that the complex-real tradeoff is related to the continuous-discrete spectrum in data modalities, where complex numbers are helpful for continuous modalities (e.g. audio, video) but not discrete (e.g. text, DNA). Real vs. Complex. Initialization . Most prior SSMs also suggest special initializations, particularly in the complex-valued case, which can help in several settings such as low-data regimes. Our default initialization for the complex case is S4D-Lin and for the real case is S4D-Real (Gu, Gupta, et al. 2022), which is based on the HIPPO theory (Gu, Dao, et al. 2020). These define the n-th element of A as −1∕2 + ni and −(n + 1) respectively. However, we expect many initializations to work fine, particularly in the large-data and real-valued SSM regimes; some ablations are considered in Section 4.6. Initialization Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. Remark 3.1. For brevity in our experimental results, we sometimes abbreviate selective SSMs as S6 models, because they are S4 models with a selection mechanism and computed with a scan. 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

The Key Differences Between Real and Complex-Valued State Space Models

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility H.2 Phillips Curve This subsection is split in two. The first part covers two alternative benchmarks. The second studies robustness at the standard settings and draws a comparison with the existing solution and empirics. Most emphasis is placed on the output slope coefficient in relation to macroeconometric estimates. H.2.1 Alternative Cases. Note that in the later case only the lagged inflation coefficient changes substantially from the text. Movements in the other coefficients are much smaller. It is interesting to look at how the cut-off value of the lag varies as the various parameters are varied. Information on the other coefficients in these cases comes in the next part. Note that the cut-off is always less than unity. [135] This reflects the fact that inflation has to be stationary. The following triptych of tables alter parameters one at a time, keeping the others at the standard setting. H.2.2 Benchmark Parametization In this subsection, I study changes in the Phillips curve slope coefficients when structural parameters are varied, over the ranges discussed in the previous section. I then perform a similar exercise for the existing solution. The difference remains stark. This should be a spur for future rigorous econometric research. The lagged inflation coefficient seems to be almost invariant to changes in the supply side, which is encouraging. The lead is also relatively unresponsive. This is encouraging from a Keynesian perspective. I anticipate the model will soon be augmented with additional frictions to better model the propagation of monetary shocks to labor and capital markets. I anticipate the model will soon be augmented with additional frictions to better model the propagation of monetary shocks to labor and capital markets. This will rest upon the robust foundation of our new understanding of inflation determination embodied in these coefficients. Overall, this part has provided a strong body of suggestive evidence that the new approximation provides a better fit than the existing coefficient by coefficient. Moreover, the stability of the critical slope coefficients, in response to plausible structural parameter uncertainty, is a showcase for robustness in macroeconomics. This exercise has surely been a useful prelude to future rigorous econometric investigation. This paper is available on arxiv under CC 4.0 license. [135] To see why this is true note that I can express the cut off as [139] This occurs when I adjust α to 0.8, when η = 1 it is higher at 0.333. [140] This conclusion is robust to any of the three deviation measures. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility H.2 Phillips Curve This subsection is split in two. The first part covers two alternative benchmarks. The second studies robustness at the standard settings and draws a comparison with the existing solution and empirics. Most emphasis is placed on the output slope coefficient in relation to macroeconometric estimates. H.2.1 Alternative Cases. H.2.1 Alternative Cases. Note that in the later case only the lagged inflation coefficient changes substantially from the text. Movements in the other coefficients are much smaller. It is interesting to look at how the cut-off value of the lag varies as the various parameters are varied. Information on the other coefficients in these cases comes in the next part. Note that the cut-off is always less than unity. [135] This reflects the fact that inflation has to be stationary. The following triptych of tables alter parameters one at a time, keeping the others at the standard setting. H.2.2 Benchmark Parametization H.2.2 Benchmark Parametization In this subsection, I study changes in the Phillips curve slope coefficients when structural parameters are varied, over the ranges discussed in the previous section. I then perform a similar exercise for the existing solution. The difference remains stark. This should be a spur for future rigorous econometric research. The lagged inflation coefficient seems to be almost invariant to changes in the supply side, which is encouraging. The lead is also relatively unresponsive. This is encouraging from a Keynesian perspective. I anticipate the model will soon be augmented with additional frictions to better model the propagation of monetary shocks to labor and capital markets. I anticipate the model will soon be augmented with additional frictions to better model the propagation of monetary shocks to labor and capital markets. This will rest upon the robust foundation of our new understanding of inflation determination embodied in these coefficients. Overall, this part has provided a strong body of suggestive evidence that the new approximation provides a better fit than the existing coefficient by coefficient. Moreover, the stability of the critical slope coefficients, in response to plausible structural parameter uncertainty, is a showcase for robustness in macroeconomics. This exercise has surely been a useful prelude to future rigorous econometric investigation. 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 [135] To see why this is true note that I can express the cut off as [139] This occurs when I adjust α to 0.8, when η = 1 it is higher at 0.333. [140] This conclusion is robust to any of the three deviation measures.

Reassessing the Phillips Curve: Benchmarking, Robustness, and Structural Variations

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables B Formalization of Tasks 1 and 2 B.1 Task 1: Textual Inference Task Given: A set of premises P = {P1, P2, . . . , Pn} and a conclusion C. Determine whether the logical entailment holds: P |= C. such as (i) The premises and conclusion are instantiated from formal syllogistic schemes and (ii) arguments may be valid or invalid. The assessment is purely on logical validity, independent of the factual correctness of the premises or conclusion. Input: Premises: A set P = {P1, P2, . . . , Pn} of premises, where each Pi is a natural language sentence derived from syllogistic schemes. Conclusion: A natural language sentence C, representing the conclusion. Output: Logical Validity Indicator: A binary output Output ∈ {True, False} such that: Here, P |= C denotes that C is a logical consequence of P. B.2 Task 2: Premise Selection Task Objective: Identify the minimal subset of premises P ′ ⊆ P that are necessary and sufficient to logically derive the conclusion C. • Given: – A set of premises P = {P1, P2, . . . , Pn}. – A conclusion C. • Task Definition: Find the minimal subset P Input: Premises: A set P = {P1, P2, . . . , Pn} containing both relevant and irrelevant (distractor) premises. Conclusion: A natural language sentence C. Output: • Selected Premises: A subset P ′ ⊆ P such that: P ′ |= C, and P ′ is minimal and necessary. Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables B Formalization of Tasks 1 and 2 B.1 Task 1: Textual Inference Task Given: A set of premises P = {P1, P2, . . . , Pn} and a conclusion C. Determine whether the logical entailment holds: P |= C. such as (i) The premises and conclusion are instantiated from formal syllogistic schemes and (ii) arguments may be valid or invalid. The assessment is purely on logical validity, independent of the factual correctness of the premises or conclusion. Input: Input: Premises : A set P = {P1, P2, . . . , Pn} of premises, where each Pi is a natural language sentence derived from syllogistic schemes. Premises Conclusion : A natural language sentence C, representing the conclusion. Conclusion Output: Output: Logical Validity Indicator: A binary output Output ∈ {True, False} such that: Logical Validity Indicator: Here, P |= C denotes that C is a logical consequence of P. B.2 Task 2: Premise Selection Task Objective : Identify the minimal subset of premises P ′ ⊆ P that are necessary and sufficient to logically derive the conclusion C. Objective • Given : Given – A set of premises P = {P1, P2, . . . , Pn}. – A conclusion C. • Task Definition : Find the minimal subset P • Task Definition Input: Input: Premises : A set P = {P1, P2, . . . , Pn} containing both relevant and irrelevant (distractor) premises. Premises Conclusion : A natural language sentence C. Conclusion Output: Output: • Selected Premises : A subset P ′ ⊆ P such that: • Selected Premises P ′ |= C, and P ′ is minimal and necessary. Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

How to Test Logical Validity in Biomedical Research

Tips For Managing Terraform Variables

Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 4.5 Nominal Equilibrium Conditions Nominal rigidity generates real distortions through the dispersion term. The demand aggregator represents price dispersion since Proposition 3. ∆ is second order when approximated from ZINSS. The first result is a global property of price dispersion, which will be called upon when analyzing the boundary conditions in the fixed-point theorem. The second is a local condition that justifies the √ ε limiting construction. Neither result is novel so proofs are relegated to Appendix B.3. The intuition is that consumers prefer variety and it is therefore costly to substitute between high and low price goods. Therefore they cannot achieve the same utility when prices are dispersed which will always arise when prices are rigid and inflation variable. Here with Calvo pricing, ∆ evolves according to the following relationship: Using Proposition 1, I can solve for the reset price to give a recursion in inflation Finally, there is the monetary policy rule. When selecting such a simple rule, my key concerns were tractability and comparability with previous work. In the next part, I confirm that including lags or leads would not be justified by welfare concerns in a purely forward-looking model. This fits with my ultimate goal of justifying inertial policy from an optimal policy standpoint. My policy analysis should prove immune to timing issues and information available to the Central Bank. This is because the boundary case of inactivity is common to all possible policy rules, whilst in a persistent stochastic setting even the expectations of future variables would induce inertia.[26] 26Taylor’s actual proposal was somewhat closer to my analysis. His rule used an inflation measure averaged over the previous four quarters. Although, his motivation was different, he envisaged that the lagged inflation rate would serve as a proxy for expected inflation. In the same conference (Henderson and McKibbin [1993]) came up with a very similar formulation. Author:
(1) David Staines. This paper is available on arxiv under CC 4.0 license. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 4.5 Nominal Equilibrium Conditions Nominal rigidity generates real distortions through the dispersion term. The demand aggregator represents price dispersion since Proposition 3. ∆ is second order when approximated from ZINSS. Proposition 3. is second order when approximated from ZINSS. The first result is a global property of price dispersion, which will be called upon when analyzing the boundary conditions in the fixed-point theorem. The second is a local condition that justifies the √ ε limiting construction. Neither result is novel so proofs are relegated to Appendix B.3. The intuition is that consumers prefer variety and it is therefore costly to substitute between high and low price goods. Therefore they cannot achieve the same utility when prices are dispersed which will always arise when prices are rigid and inflation variable. Here with Calvo pricing, ∆ evolves according to the following relationship: Using Proposition 1, I can solve for the reset price to give a recursion in inflation Finally, there is the monetary policy rule. When selecting such a simple rule, my key concerns were tractability and comparability with previous work. In the next part, I confirm that including lags or leads would not be justified by welfare concerns in a purely forward-looking model. This fits with my ultimate goal of justifying inertial policy from an optimal policy standpoint. My policy analysis should prove immune to timing issues and information available to the Central Bank. This is because the boundary case of inactivity is common to all possible policy rules, whilst in a persistent stochastic setting even the expectations of future variables would induce inertia.[26] 26Taylor’s actual proposal was somewhat closer to my analysis. His rule used an inflation measure averaged over the previous four quarters. Although, his motivation was different, he envisaged that the lagged inflation rate would serve as a proxy for expected inflation. In the same conference (Henderson and McKibbin [1993]) came up with a very similar formulation. Author: (1) David Staines. Author: Author: (1) David Staines. 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

Why Inflation and Price Variations Matter for Economic Stability

My girl and I were enjoying one of the first days of a well-earned week of vacation at the sun-drenched Black Sea seaside. Damn near everything was downright paradisiacal until (as you do) we got into a little argument... Here’s the crux of the argument: Being on vacation, you’re inclined to take a lot of photos of yourselves looking fabulous and enjoying the colorful environs you find yourself in, and my girl was a bit upset that I wasn’t uploading more photos to social media of us looking like a cute couple as she was doing. Most of the photos I was uploading were just of me looking adventuresome and generally being museful in nature. In the past, a large marriage ceremony was the way a couple made a public declaration of love to one another; now, we do it by instantly broadcasting JPEGs around the world. Obviously, my girl wants that validation. This is my reasoning… I don’t want to drag her into the public space with me — The internet can be rude and nasty for public intellectuals who take controversial or unpopular positions as I sometimes do. I don’t want her ever to be harassed because of my opinions. The fewer photos and digital paper trails linking us, the better. Different public intellectuals I follow, like Stefan Molyneux, Joe Rogan, or Sam Harris, are careful to keep their loved ones’ images and names hard to find. Mindset guru and culture warrior extraordinaire Mike Cernovich has exposed his lovely wife to deranged harassment campaigns by opening a window into her life with social media. Look at all the harassment and hatred Melania Trump faces. I’d rather maintain a modicum of separation between my public and private life.


I don’t want to feed her vanity too much with an allowance of social media cross-promotion — Likes, hearts, followers, and “friends” are currency in our narcissistic era, especially for women who just have this voracious appetite for validation. Many e-celeb guys do this thing where they pay women to be in their lives with social media attention — I’m far from being a real e-celeb. Still, I know just how intoxicating a deluge of likes, comments, and followers can be (especially for women), and I don’t want to put my girl on that dopamine treadmill to nowhere. Constant social media validation for women is the same thing as porn addiction is for men — it makes them shallow and vapid and poisons any chance of a healthy pair-bonded relationship. I want to be loved and respected for my virtues, personality, and accomplishments - not because of the vague possibility of internet stardom that I can dangle before someone.


I want to limit the encroachment of social media into our relationship — social media makes humans weird, narcissistic, envious, shallow, unfocused, unhappy, and stupid — I do a pretty effective working memory rehab regimen to block the adverse side effects of being a social media power user. I have yet to convince my girl to habituate a similar mindfulness regimen, though, and I don’t want to use social media in a grotesque way to stimulate jealousy and envy among those who follow me. Social Media Compatibility Relationship gurus have long told us that we should, on the first or second date, ascertain the political compatibility of our potential partners. I’d urge the same when it comes to social media usage. If one person is a social media addict and the other hates social media, that sure seems like an irreconcilable difference — why waste time? Delineate your social media following If you use Facebook, create a separate friends list of people you would actually like to hear from and stay in touch with who you wouldn’t mind peeking into your personal life. Facebook gives you the option of delineating who you share what with. Sure, if the CIA is out to get you, they can probably get a hold of your most scandalous social media content anyway, but by practicing discretion using Facebook’s settings, you mitigate a lot of drama and heartache. Many people just opt to leave Facebook, but for many of us, it has become an essential tool for interfacing with the modern world; don’t flippantly broadcast your most intimate moments and feelings to everyone whose friend request you might accept. My girl and I were enjoying one of the first days of a well-earned week of vacation at the sun-drenched Black Sea seaside. Damn near everything was downright paradisiacal until ( as you do ) we got into a little argument... as you do Here’s the crux of the argument: Being on vacation, you’re inclined to take a lot of photos of yourselves looking fabulous and enjoying the colorful environs you find yourself in, and my girl was a bit upset that I wasn’t uploading more photos to social media of us looking like a cute couple as she was doing. Most of the photos I was uploading were just of me looking adventuresome and generally being museful in nature. In the past, a large marriage ceremony was the way a couple made a public declaration of love to one another; now, we do it by instantly broadcasting JPEGs around the world. Obviously, my girl wants that validation. This is my reasoning… I don’t want to drag her into the public space with me — The internet can be rude and nasty for public intellectuals who take controversial or unpopular positions as I sometimes do. I don’t want her ever to be harassed because of my opinions. The fewer photos and digital paper trails linking us, the better. Different public intellectuals I follow, like Stefan Molyneux, Joe Rogan, or Sam Harris, are careful to keep their loved ones’ images and names hard to find. Mindset guru and culture warrior extraordinaire Mike Cernovich has exposed his lovely wife to deranged harassment campaigns by opening a window into her life with social media. Look at all the harassment and hatred Melania Trump faces. I’d rather maintain a modicum of separation between my public and private life. I don’t want to feed her vanity too much with an allowance of social media cross-promotion — Likes, hearts, followers, and “friends” are currency in our narcissistic era, especially for women who just have this voracious appetite for validation. Many e-celeb guys do this thing where they pay women to be in their lives with social media attention — I’m far from being a real e-celeb. Still, I know just how intoxicating a deluge of likes, comments, and followers can be (especially for women), and I don’t want to put my girl on that dopamine treadmill to nowhere. Constant social media validation for women is the same thing as porn addiction is for men — it makes them shallow and vapid and poisons any chance of a healthy pair-bonded relationship. I want to be loved and respected for my virtues, personality, and accomplishments - not because of the vague possibility of internet stardom that I can dangle before someone. I want to limit the encroachment of social media into our relationship — social media makes humans weird, narcissistic, envious, shallow, unfocused, unhappy, and stupid — I do a pretty effective working memory rehab regimen to block the adverse side effects of being a social media power user. I have yet to convince my girl to habituate a similar mindfulness regimen, though, and I don’t want to use social media in a grotesque way to stimulate jealousy and envy among those who follow me. I don’t want to drag her into the public space with me — The internet can be rude and nasty for public intellectuals who take controversial or unpopular positions as I sometimes do. I don’t want her ever to be harassed because of my opinions. The fewer photos and digital paper trails linking us, the better. Different public intellectuals I follow, like Stefan Molyneux, Joe Rogan, or Sam Harris, are careful to keep their loved ones’ images and names hard to find. Mindset guru and culture warrior extraordinaire Mike Cernovich has exposed his lovely wife to deranged harassment campaigns by opening a window into her life with social media. Look at all the harassment and hatred Melania Trump faces. I’d rather maintain a modicum of separation between my public and private life. I don’t want to drag her into the public space with me — The internet can be rude and nasty for public intellectuals who take controversial or unpopular positions as I sometimes do. I don’t want her ever to be harassed because of my opinions. The fewer photos and digital paper trails linking us, the better. Different public intellectuals I follow, like Stefan Molyneux, Joe Rogan, or Sam Harris, are careful to keep their loved ones’ images and names hard to find. Mindset guru and culture warrior extraordinaire Mike Cernovich has exposed his lovely wife to deranged harassment campaigns by opening a window into her life with social media. Look at all the harassment and hatred Melania Trump faces. I’d rather maintain a modicum of separation between my public and private life. I don’t want to drag her into the public space with me I don’t want to feed her vanity too much with an allowance of social media cross-promotion — Likes, hearts, followers, and “friends” are currency in our narcissistic era, especially for women who just have this voracious appetite for validation. Many e-celeb guys do this thing where they pay women to be in their lives with social media attention — I’m far from being a real e-celeb. Still, I know just how intoxicating a deluge of likes, comments, and followers can be (especially for women), and I don’t want to put my girl on that dopamine treadmill to nowhere. Constant social media validation for women is the same thing as porn addiction is for men — it makes them shallow and vapid and poisons any chance of a healthy pair-bonded relationship. I want to be loved and respected for my virtues, personality, and accomplishments - not because of the vague possibility of internet stardom that I can dangle before someone. I don’t want to feed her vanity too much with an allowance of social media cross-promotion — Likes , hearts, followers, and “friends” are currency in our narcissistic era, especially for women who just have this voracious appetite for validation. Many e-celeb guys do this thing where they pay women to be in their lives with social media attention — I’m far from being a real e-celeb. Still, I know just how intoxicating a deluge of likes, comments, and followers can be (especially for women), and I don’t want to put my girl on that dopamine treadmill to nowhere . Constant social media validation for women is the same thing as porn addiction is for men — it makes them shallow and vapid and poisons any chance of a healthy pair-bonded relationship. I want to be loved and respected for my virtues, personality, and accomplishments - not because of the vague possibility of internet stardom that I can dangle before someone. I don’t want to feed her vanity too much with an allowance of social media cross-promotion Likes currency pay treadmill to nowhere I want to limit the encroachment of social media into our relationship — social media makes humans weird, narcissistic, envious, shallow, unfocused, unhappy, and stupid — I do a pretty effective working memory rehab regimen to block the adverse side effects of being a social media power user. I have yet to convince my girl to habituate a similar mindfulness regimen, though, and I don’t want to use social media in a grotesque way to stimulate jealousy and envy among those who follow me. I want to limit the encroachment of social media into our relationship — social media makes humans weird, narcissistic, envious, shallow, unfocused, unhappy, and stupid — I do a pretty effective working memory rehab regimen to block the adverse side effects of being a social media power user. I have yet to convince my girl to habituate a similar mindfulness regimen, though, and I don’t want to use social media in a grotesque way to stimulate jealousy and envy among those who follow me. I want to limit the encroachment of social media into our relationship unhappy, and stupid Social Media Compatibility Relationship gurus have long told us that we should, on the first or second date, ascertain the political compatibility of our potential partners. I’d urge the same when it comes to social media usage. If one person is a social media addict and the other hates social media, that sure seems like an irreconcilable difference — why waste time? If one person is a social media addict and the other hates social media, that sure seems like an irreconcilable difference — why waste time? why waste time? Delineate your social media following If you use Facebook, create a separate friends list of people you would actually like to hear from and stay in touch with who you wouldn’t mind peeking into your personal life. Facebook gives you the option of delineating who you share what with. Sure, if the CIA is out to get you, they can probably get a hold of your most scandalous social media content anyway, but by practicing discretion using Facebook’s settings, you mitigate a lot of drama and heartache. Many people just opt to leave Facebook, but for many of us, it has become an essential tool for interfacing with the modern world; don’t flippantly broadcast your most intimate moments and feelings to everyone whose friend request you might accept . create a separate friends list don’t flippantly broadcast your most intimate moments and feelings to everyone whose friend request you might accept

Social Media Sanity in Relationships is Possible (Even on Vacation)

Helm chart validation has traditionally relied on JSON Schema, which, while functional, can be limiting when you need more expressive validation rules. Enter the Helm CEL Plugin - a powerful new tool that leverages Common Expression Language (CEL) to provide more flexible and powerful validation capabilities for your Helm charts. What is CEL? Common Expression Language (CEL) is a simple, powerful expression language created by Google that makes it easy to write complex validation rules. With CEL, you can write more intuitive and expressive validation rules compared to JSON Schema while maintaining excellent performance and safety guarantees. Getting Started Installation is straightforward using Helm's plugin management system: helm plugin install https://github.com/idsulik/helm-cel Core Features Value Validation The plugin introduces a new command for validating your chart values: helm cel validate ./mychart You can specify custom files for both values and rules: helm cel validate ./mychart --values-file prod.values.yaml --rules-file prod.cel.yaml Rule Generation One of the standout features is the ability to generate validation rules based on your existing values structure automatically: helm cel generate ./mychart This is particularly useful when starting with a new chart or when you want to create a baseline set of rules that you can then customize. Writing Validation Rules The rules are defined in a values.cel.yaml file, with each rule consisting of: A CEL expression that must evaluate to true for valid values
A description explaining what the rule validates
An optional severity level Here's a practical example: rules:
 - expr: "has(values.service) && has(values.service.port)"
 desc: "service port is required"
 
 - expr: "values.service.port >= 1 && values.service.port <= 65535"
 desc: "service port must be between 1 and 65535"
 severity: warning Advanced Features Severity Levels The plugin supports two severity levels: error: Fails validation (default)
warning: Shows a warning but allows validation to pass This flexibility allows you to implement both strict requirements and best-practice recommendations in your validation rules. Reusable Expressions To keep your rules DRY (Don't Repeat Yourself), you can define reusable expressions: expressions:
 portRange: 'values.service.port >= 1 && values.service.port <= 65535'
 nodePortRange: 'values.service.nodePort >= 30000 && values.service.nodePort <= 32767'

rules:
 - expr: "${portRange}"
 desc: "Service port must be valid"
 
 - expr: 'values.service.type == "NodePort" ? ${nodePortRange} : true'
 desc: "NodePort must be valid when type is NodePort" Common Validation Patterns Here are some practical examples of common validation scenarios: Required Fields - expr: "has(values.fieldName)"
 desc: "fieldName is required" Numeric Ranges - expr: "values.number >= 0 && values.number <= 100"
 desc: "number must be between 0 and 100" Type Checking - expr: "type(values.ports) == list"
 desc: "ports must be a list" Resource Specifications - expr: 'values.resources.requests.memory.matches("^[0-9]+(Mi|Gi)$")'
 desc: "memory requests must be in Mi or Gi" Clear Feedback The plugin provides clear, actionable feedback when validation fails: ❌ Validation failed: replica count must be at least 1
 Rule: values.replicaCount >= 1
 Path: replicaCount
 Current value: 0 For warnings, you'll see: ⚠️ Service port must be between 1 and 65535
 Rule: values.service.port >= 1 && values.service.port <= 65535
 Path: service.port
 Current value: 80801 Why Choose Helm CEL Plugin? More Expressive: CEL provides a more natural and powerful way to express validation rules compared to JSON Schema.
Better Error Messages: Get clear, actionable feedback when validation fails.
Flexible Severity Levels: Distinguish between hard requirements and best practices.
Rule Generation: Automatically generate baseline rules from existing values.
Reusable Expressions: Keep your validation rules DRY and maintainable. Conclusion The Helm CEL Plugin brings a new level of sophistication to Helm chart validation. By leveraging the power of CEL, it provides a more expressive and maintainable way to ensure your Helm charts are configured correctly. Whether you're managing a single chart or maintaining a large repository of charts, this plugin can help you catch configuration issues early and ensure consistency across your Kubernetes deployments. If you're tired of the limitations of JSON Schema validation, give the Helm CEL Plugin a try. Your future self (and your team) will thank you. Helm chart validation has traditionally relied on JSON Schema, which, while functional, can be limiting when you need more expressive validation rules. Enter the Helm CEL Plugin - a powerful new tool that leverages Common Expression Language (CEL) to provide more flexible and powerful validation capabilities for your Helm charts. What is CEL? Common Expression Language (CEL) is a simple, powerful expression language created by Google that makes it easy to write complex validation rules. With CEL, you can write more intuitive and expressive validation rules compared to JSON Schema while maintaining excellent performance and safety guarantees. Getting Started Installation is straightforward using Helm's plugin management system: helm plugin install https://github.com/idsulik/helm-cel helm plugin install https://github.com/idsulik/helm-cel Core Features Value Validation The plugin introduces a new command for validating your chart values: helm cel validate ./mychart helm cel validate ./mychart You can specify custom files for both values and rules: helm cel validate ./mychart --values-file prod.values.yaml --rules-file prod.cel.yaml helm cel validate ./mychart --values-file prod.values.yaml --rules-file prod.cel.yaml Rule Generation One of the standout features is the ability to generate validation rules based on your existing values structure automatically: helm cel generate ./mychart helm cel generate ./mychart This is particularly useful when starting with a new chart or when you want to create a baseline set of rules that you can then customize. Writing Validation Rules The rules are defined in a values.cel.yaml file, with each rule consisting of: values.cel.yaml A CEL expression that must evaluate to true for valid values A description explaining what the rule validates An optional severity level A CEL expression that must evaluate to true for valid values true A description explaining what the rule validates An optional severity level Here's a practical example: rules:
 - expr: "has(values.service) && has(values.service.port)"
 desc: "service port is required"
 
 - expr: "values.service.port >= 1 && values.service.port <= 65535"
 desc: "service port must be between 1 and 65535"
 severity: warning rules:
 - expr: "has(values.service) && has(values.service.port)"
 desc: "service port is required"
 
 - expr: "values.service.port >= 1 && values.service.port <= 65535"
 desc: "service port must be between 1 and 65535"
 severity: warning Advanced Features Severity Levels The plugin supports two severity levels: error: Fails validation (default) warning: Shows a warning but allows validation to pass error : Fails validation (default) error warning : Shows a warning but allows validation to pass warning This flexibility allows you to implement both strict requirements and best-practice recommendations in your validation rules. Reusable Expressions To keep your rules DRY (Don't Repeat Yourself), you can define reusable expressions: expressions:
 portRange: 'values.service.port >= 1 && values.service.port <= 65535'
 nodePortRange: 'values.service.nodePort >= 30000 && values.service.nodePort <= 32767'

rules:
 - expr: "${portRange}"
 desc: "Service port must be valid"
 
 - expr: 'values.service.type == "NodePort" ? ${nodePortRange} : true'
 desc: "NodePort must be valid when type is NodePort" expressions:
 portRange: 'values.service.port >= 1 && values.service.port <= 65535'
 nodePortRange: 'values.service.nodePort >= 30000 && values.service.nodePort <= 32767'

rules:
 - expr: "${portRange}"
 desc: "Service port must be valid"
 
 - expr: 'values.service.type == "NodePort" ? ${nodePortRange} : true'
 desc: "NodePort must be valid when type is NodePort" Common Validation Patterns Here are some practical examples of common validation scenarios: Required Fields Required Fields Required Fields - expr: "has(values.fieldName)"
 desc: "fieldName is required" - expr: "has(values.fieldName)"
 desc: "fieldName is required" Numeric Ranges Numeric Ranges Numeric Ranges - expr: "values.number >= 0 && values.number <= 100"
 desc: "number must be between 0 and 100" - expr: "values.number >= 0 && values.number <= 100"
 desc: "number must be between 0 and 100" Type Checking Type Checking Type Checking - expr: "type(values.ports) == list"
 desc: "ports must be a list" - expr: "type(values.ports) == list"
 desc: "ports must be a list" Resource Specifications Resource Specifications Resource Specifications - expr: 'values.resources.requests.memory.matches("^[0-9]+(Mi|Gi)$")'
 desc: "memory requests must be in Mi or Gi" - expr: 'values.resources.requests.memory.matches("^[0-9]+(Mi|Gi)$")'
 desc: "memory requests must be in Mi or Gi" Clear Feedback The plugin provides clear, actionable feedback when validation fails: ❌ Validation failed: replica count must be at least 1
 Rule: values.replicaCount >= 1
 Path: replicaCount
 Current value: 0 ❌ Validation failed: replica count must be at least 1
 Rule: values.replicaCount >= 1
 Path: replicaCount
 Current value: 0 For warnings, you'll see: ⚠️ Service port must be between 1 and 65535
 Rule: values.service.port >= 1 && values.service.port <= 65535
 Path: service.port
 Current value: 80801 ⚠️ Service port must be between 1 and 65535
 Rule: values.service.port >= 1 && values.service.port <= 65535
 Path: service.port
 Current value: 80801 Why Choose Helm CEL Plugin? More Expressive: CEL provides a more natural and powerful way to express validation rules compared to JSON Schema. Better Error Messages: Get clear, actionable feedback when validation fails. Flexible Severity Levels: Distinguish between hard requirements and best practices. Rule Generation: Automatically generate baseline rules from existing values. Reusable Expressions: Keep your validation rules DRY and maintainable. More Expressive : CEL provides a more natural and powerful way to express validation rules compared to JSON Schema. More Expressive Better Error Messages : Get clear, actionable feedback when validation fails. Better Error Messages Flexible Severity Levels : Distinguish between hard requirements and best practices. Flexible Severity Levels Rule Generation : Automatically generate baseline rules from existing values. Rule Generation Reusable Expressions : Keep your validation rules DRY and maintainable. Reusable Expressions Conclusion The Helm CEL Plugin brings a new level of sophistication to Helm chart validation. By leveraging the power of CEL, it provides a more expressive and maintainable way to ensure your Helm charts are configured correctly. Whether you're managing a single chart or maintaining a large repository of charts, this plugin can help you catch configuration issues early and ensure consistency across your Kubernetes deployments. Helm CEL Plugin If you're tired of the limitations of JSON Schema validation, give the Helm CEL Plugin a try. Your future self (and your team) will thank you.

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