Rankings on the AI 1000 are based on HG’s patent-pending AI Maturity Index TM, which includes insights from a specific company’s AI product adoption, the locations where adoption is detected, the strength of these detections, and the application of an ...

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HG Insights Publishes the First Annual AI 1000TM, Providing Insights into Individual Companies’ AI Maturity

Hg Fusion has been established in partnership with S64, integrating its private markets technology platform to deliver scalable access and end-to-end lifecycle management for wealth clients across Europe and Asia. To find out more about how private investors could gain access to Hg's investment opportunities, visit www.hgwealth.com

Hg launches Fusion to enable investors access to Hg's investment strategies through a single investment

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Value of spending on corporate social responsibility (CSR) in India in financial year 2022, by company type (in billion Indian rupees)

Corporate social responsibility (CSR) spending in India FY 2022, by company type

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Analysts Offer Insights on Materials Companies: Barrick Gold (GOLD), Entegris (ENTG) and Commercial Metals Company (CMC)

Intricately (An HG Insights Company)

intricately

When Coding Determines the Survival of The City

Kodlaşdırma şəhərin sağ qalmasını təyin edərkən

Калі кадзіраванне вызначае выжыванне горада

Kada kodiranje određuje opstanak grada

Cuando la codificación determina la supervivencia de la ciudad

Kun koodaus määrittää kaupungin selviytymisen

Lè kodaj detèmine siviv vil la

コーディングが都市の存続を決定するとき

जब कोडिङले सहरको अस्तित्व निर्धारण गर्छ

Marka Codayntu Go'aamiso Badbaadada Magaalada

Коли кодування визначає виживання міста

当编码决定城市的生存

Lapho Ukufakwa Kwekhodi Kunquma Ukusinda Kwedolobha

Authors:
(1) Vinicius Yu Okubo, B.S. in electrical engineering from the University of São Paulo in 2022 and currently, he is pursuing his M.S. in electrical engineering at the University of São Paulo;
(2) Kotaro Shimizu, B.S. degree in Physics from Waseda University, Japan, in 2019 and M.S. degree in Physics from the University of Tokyo, Japan, 2021 and He has been pursuing his Ph.D. in Physics as a JSPS research fellowship for young scientists in the University of Tokyo since 2021;
(3) B.S. Shivaram, received his B.S. degree in Physics, Chemistry and Mathematics from Bangalore University, India, in 1977 and the M.S. degree in Physics from the Indian Institute of Technology, Madras, India, in 1979 and his Ph.D. in experimental condensed matter physics from Northwestern University, Evanston, Illinois in 1984;
(4) Hae Yong Kim, He received the B.S. and M.S. degrees (with distinctions) in computer science and the Ph.D. degree in electrical engineering from the Universidade de São Paulo (USP), Brazil, in 1988, 1992 and 1997, respectively. Table of Links Abstract and I Introduction II. Related Works III Methodology IV Experiments and Results V Conclusion and References IV. EXPERIMENTS AND RESULTS A. PERFORMANCE EVALUATION Evaluating TM-CNN poses a certain challenge because there are no benchmarks or published results for direct comparison. So, we defined that junction and terminal detections reviewed by human observers as the gold standard and compared the algorithms against this standard. For testing purposes, we annotated 5 independent images from varying experiments, using the same procedure as the training images (subsection III-E). False detections were excluded since they do not contribute to the detection evaluation. In total, the test set contained 3,551 junctions and 3,653 terminals. B. RESULTS FROM A PHYSICS PERSPECTIVE In Bi:YIG films, it is known that a perfect stripe pattern is the most energetically favorable and stable configuration of magnetic moments [4]. In this structure, the magnetic moments exhibit stripes aligning straight in one direction throughout the entire structure, resulting in large spatial coherence. Meanwhile, the experimentally obtained structures often manifest labyrinthine patterns, as illustrated by dark and bright stripes in Fig. 1. In these labyrinthine patterns, stripes are aligned within small domains but propagate in different directions between different domains, and thus the spatial coherence of stripes is smaller than the perfect stripe. Notably, junctions and terminals are defects emerging in the formation of these labyrinthine patterns, intricately linked to the growth of spatial coherence in the system. Introducing such defects into the perfect stripe structure leads to the bending and branching of the initially aligned stripes, eventually reducing spatial coherence accompanied by the transformation to labyrinthine patterns. Indeed, a plethora of defects are observed in the labyrinthine patterns shown in Fig. 2. Conversely, the elimination of these defects plays a crucial role in the transition from a quenched state to an annealed state, where the system increases the spatial coherence. Each of these defects cannot be eliminated alone by continuous deformation of the stripes due to their topological properties. They can be removed through the pair annihilation. For instance, in Fig. 2, terminals are branched from junctions and they can be removed pairwisely by reducing the branching distance. These defects, known as topological defects, are widely recognized for playing a crucial role in the formation of periodic structures. By detecting the positions of defects, the evolution of the labyrinthine patterns would be systematically quantified. Specifically in the context of magnetic labyrinthine patterns, a much smaller dataset was used to detect these defects manually [4]. Recently, persistent homology has been used to extract the topological features of the labyrinthine patterns in a systematic way [38]. However, it is still difficult to detect the positions of defects and classify them into junctions and terminals. In this article, we achieved the systematic detection. To quantify the evolution of the labyrinthine patterns, we applied the TM-CNN technique to the 444 images and investigated how the number of defects and their locations changes step by step. Fig. 6 illustrates the step dependence of the number of junctions and terminals during the demagnetization process. Results from 6 different experimental runs were averaged to estimate the number of defects. In the initial stages of the demagnetization process, there are approximately 800 defects. The number of defects sharply decreases around step 5 and remains nearly unchanged from step 10 onward. Consequently, the transition to the quenched state is inferred to start before step 5 and be completed by step 10. The reduction in the number of defects during the demagnetization process is compatible with the naive expectation based on the physical argument of a decrease in the number of defects associated with the increased spatial coherence of the stripes. Furthermore, the number of junctions and terminals are close, which is consistent with the topological argument that junctions and terminals are paired. Further quantification of the evolution of the labyrinthine patterns based on the spatial distribution of the defects (and not only on their number) is discussed in another article [6]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Vinicius Yu Okubo, B.S. in electrical engineering from the University of São Paulo in 2022 and currently, he is pursuing his M.S. in electrical engineering at the University of São Paulo; (2) Kotaro Shimizu, B.S. degree in Physics from Waseda University, Japan, in 2019 and M.S. degree in Physics from the University of Tokyo, Japan, 2021 and He has been pursuing his Ph.D. in Physics as a JSPS research fellowship for young scientists in the University of Tokyo since 2021; (3) B.S. Shivaram, received his B.S. degree in Physics, Chemistry and Mathematics from Bangalore University, India, in 1977 and the M.S. degree in Physics from the Indian Institute of Technology, Madras, India, in 1979 and his Ph.D. in experimental condensed matter physics from Northwestern University, Evanston, Illinois in 1984; (4) Hae Yong Kim, He received the B.S. and M.S. degrees (with distinctions) in computer science and the Ph.D. degree in electrical engineering from the Universidade de São Paulo (USP), Brazil, in 1988, 1992 and 1997, respectively. Authors: Authors: (1) Vinicius Yu Okubo, B.S. in electrical engineering from the University of São Paulo in 2022 and currently, he is pursuing his M.S. in electrical engineering at the University of São Paulo; (2) Kotaro Shimizu, B.S. degree in Physics from Waseda University, Japan, in 2019 and M.S. degree in Physics from the University of Tokyo, Japan, 2021 and He has been pursuing his Ph.D. in Physics as a JSPS research fellowship for young scientists in the University of Tokyo since 2021; (3) B.S. Shivaram, received his B.S. degree in Physics, Chemistry and Mathematics from Bangalore University, India, in 1977 and the M.S. degree in Physics from the Indian Institute of Technology, Madras, India, in 1979 and his Ph.D. in experimental condensed matter physics from Northwestern University, Evanston, Illinois in 1984; (4) Hae Yong Kim, He received the B.S. and M.S. degrees (with distinctions) in computer science and the Ph.D. degree in electrical engineering from the Universidade de São Paulo (USP), Brazil, in 1988, 1992 and 1997, respectively. Table of Links Abstract and I Introduction Abstract and I Introduction II. Related Works II. Related Works III Methodology III Methodology IV Experiments and Results IV Experiments and Results V Conclusion and References V Conclusion and References IV. EXPERIMENTS AND RESULTS A. PERFORMANCE EVALUATION A. PERFORMANCE EVALUATION Evaluating TM-CNN poses a certain challenge because there are no benchmarks or published results for direct comparison. So, we defined that junction and terminal detections reviewed by human observers as the gold standard and compared the algorithms against this standard. For testing purposes, we annotated 5 independent images from varying experiments, using the same procedure as the training images (subsection III-E). False detections were excluded since they do not contribute to the detection evaluation. In total, the test set contained 3,551 junctions and 3,653 terminals. B. RESULTS FROM A PHYSICS PERSPECTIVE B. RESULTS FROM A PHYSICS PERSPECTIVE In Bi:YIG films, it is known that a perfect stripe pattern is the most energetically favorable and stable configuration of magnetic moments [4]. In this structure, the magnetic moments exhibit stripes aligning straight in one direction throughout the entire structure, resulting in large spatial coherence. Meanwhile, the experimentally obtained structures often manifest labyrinthine patterns, as illustrated by dark and bright stripes in Fig. 1. In these labyrinthine patterns, stripes are aligned within small domains but propagate in different directions between different domains, and thus the spatial coherence of stripes is smaller than the perfect stripe. Notably, junctions and terminals are defects emerging in the formation of these labyrinthine patterns, intricately linked to the growth of spatial coherence in the system. Introducing such defects into the perfect stripe structure leads to the bending and branching of the initially aligned stripes, eventually reducing spatial coherence accompanied by the transformation to labyrinthine patterns. Indeed, a plethora of defects are observed in the labyrinthine patterns shown in Fig. 2. Conversely, the elimination of these defects plays a crucial role in the transition from a quenched state to an annealed state, where the system increases the spatial coherence. Each of these defects cannot be eliminated alone by continuous deformation of the stripes due to their topological properties. They can be removed through the pair annihilation. For instance, in Fig. 2, terminals are branched from junctions and they can be removed pairwisely by reducing the branching distance. These defects, known as topological defects, are widely recognized for playing a crucial role in the formation of periodic structures. By detecting the positions of defects, the evolution of the labyrinthine patterns would be systematically quantified. Specifically in the context of magnetic labyrinthine patterns, a much smaller dataset was used to detect these defects manually [4]. Recently, persistent homology has been used to extract the topological features of the labyrinthine patterns in a systematic way [38]. However, it is still difficult to detect the positions of defects and classify them into junctions and terminals. In this article, we achieved the systematic detection. To quantify the evolution of the labyrinthine patterns, we applied the TM-CNN technique to the 444 images and investigated how the number of defects and their locations changes step by step. Fig. 6 illustrates the step dependence of the number of junctions and terminals during the demagnetization process. Results from 6 different experimental runs were averaged to estimate the number of defects. In the initial stages of the demagnetization process, there are approximately 800 defects. The number of defects sharply decreases around step 5 and remains nearly unchanged from step 10 onward. Consequently, the transition to the quenched state is inferred to start before step 5 and be completed by step 10. The reduction in the number of defects during the demagnetization process is compatible with the naive expectation based on the physical argument of a decrease in the number of defects associated with the increased spatial coherence of the stripes. Furthermore, the number of junctions and terminals are close, which is consistent with the topological argument that junctions and terminals are paired. Further quantification of the evolution of the labyrinthine patterns based on the spatial distribution of the defects (and not only on their number) is discussed in another article [6]. 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

Efficient Detection of Defects in Magnetic Labyrinthine Patterns: Experiments and Results

Decentralized Micro-Governance Model for the Kingdom of Tonga Based on Proof-of-Work Consensus

The Critical Role of Data Annotation in Shaping the Future of Generative AI

Крытычна важная роля анатацыі даных у фарміраванні будучыні генератыўнага ІІ

Критичната роля на анотирането на данни при оформянето на бъдещето на генериращия ИИ

Den kritiske rolle for dataannotering i at forme fremtiden for generativ AI

El papel fundamental de la anotación de datos en la configuración del futuro de la IA generativa

Le rôle crucial de l’annotation des données dans la définition de l’avenir de l’IA générative

Il ruolo critico dell'annotazione dei dati nel plasmare il futuro dell'intelligenza artificiale generativa

生成型 AI の未来を形作るデータ注釈の重要な役割

생성 AI의 미래를 형성하는 데 있어서 데이터 주석의 중요한 역할

Karolo ye Bohlokwa ya Tlhaloso ya Datha go Bopeng Bokamoso bja AI ya Tlhagišo

Rolul critic al adnotării datelor în modelarea viitorului IA generativă

Rozhodujúca úloha anotácie údajov pri formovaní budúcnosti generatívnej AI

Den kritiska rollen för datakommentarer i att forma framtiden för generativ AI

We are excited to announce the brand upgrade and a complete UI/UX overhaul of LFG!, transforming it into a cutting-edge Web3 Super App. This evolution marks a new chapter as we become the ultimate platform for education, entertainment, gaming, and trading in the Web3 space. Our mission? To onboard the first billion users into the Web3 ecosystem by offering a seamless, multifaceted experience that brings together the best of Web3 all in one place. Join us on this exciting journey into the future of decentralized tech! Super App as the Traffic Nexus: LFG! aims to expand its content, entertainment, and educational ecosystem centered around the Super App, driving active user engagement for sustained long-term growth and creating a virtuous cycle of traffic and participation. There are three Pillars of the LFG Ecosystem: Learning Hub: Immerse yourself in our wealth of Web3 content that is meticulously curated by AI. Our intelligent algorithms sift through the web to surface the highest-quality resources, tailoring recommendations to align with your interests and preferences. This personalized approach ensures that users are not only empowered with the most relevant and up-to-date industry insights but also provided with the most direct learning pathways, optimizing the learning experience and ensuring that users stay ahead of the curve in the ever-evolving Web3 landscape.


Mining Hub: Newcomers are introduced to the Web3 world with an array of mining options intricately linked to future LFG token airdrops. Earn Mining points through community engagement and various platform activities, strategically designed to educate, entertain, and reward users as they explore the Web3 landscape. Our mining experiences, made possible through our strategic partnerships with leading industry exchanges and project owners, offer an experience of the earning process to help users understand Web3.


Game Hub: Our platform, a premier destination teeming with an extensive library of games, is committed to enriching the entertainment aspect of Web3 with a variety of engaging and educational game genres, designed to delight and inform players in an interactive setting. We're excited to announce the upcoming release of our gaming SDK, offering project partners unique collaboration opportunities to launch customized LFG games on our platform and attract a diverse array of high-quality games in to LFG via our SDK, further expanding our already robust gaming reserve and solidifying LFG's position as a leading entertainment hub in the Web3 space. Key Features of the New LFG! Experience: Create, Learn & Earn: Users can learn from the outstanding content generated on LFG and support their favorite creators by liking and sharing their work, thus creators can earn the rewards by incentives.
Explore & Collect: Discover a vast array of Web3 videos and news blogs, and earn rewards while staying updated on industry trends.
Social Growth: Invite friends to join LFG! and earn commissions from their in-app activities, fostering a collaborative growth journey in the Web3 space.
LFG Points & Token Airdrops: Earn LFG Points that determine your share of future LFG token airdrops, reflecting your active participation in the ecosystem. Exciting New User Engagement Initiatives: Invitation Treasure Hunt: Engage with our community and unlock exclusive opportunities by inviting new members to the LFG platform. For friends you invite, you and your friends will earn a chance to participate in our treasure box lottery, offering a thrilling spin of fortune in your favor. Wheel of Fortune Frenzy: Spin your way to rewards with our Wheel of Fortune event. Users can earn spins by extending their network or by utilizing their hard-earned points, blending the joy of participation with the anticipation of winning. Point Leaderboard Challenge: Compete for supremacy on our point leaderboard and share in the cash bounty. Participate in our daily sprint event from 8 PM to 10 PM for a chance to climb the ranks and claim your share of the prize pool. Plus, leverage your networking prowess to earn invitation points and divide an additional pool of rewards. Gaming Mastery Marathon: Demonstrate your gaming prowess and climb the leaderboards in our mini-games for a chance to share cash rewards and LFG points. Complete designated in-game tasks to earn bonus points, adding an extra layer of reward to your gaming achievements. LFG is committed to fostering a vibrant community that thrives on interaction, competition, and reward. These new initiatives are designed to not only enrich the user experience but also to solidify LFG as a leading platform in the Web3 space. Seamless Integration for a Connected Future: LFG! is more than an app; it's a super app with a super vision for the connected future, seamlessly bridging the gap between Web2 and Web3. Designed as a comprehensive platform for edutainment, LFG! offers a space where users can explore, learn, play, and earn within the dynamic Web3 ecosystem. We are committed to fostering a global shift towards a more inclusive, knowledgeable, and rewarding digital economy. As we expand our ecosystem, LFG! will integrate social networking, signal communities, utility applications and other functional market tools, providing a multi-dimensional user experience that enhances. Become an OG LFG member today to unlock a world of exclusive benefits, airdrop rewards, and a front-row seat to the future of digital interaction. With our innovative approach, LFG! is not just a platform but a catalyst for transformation, setting the stage for a new era of Web3 engagement. Join us on this journey as we continue to innovate, ensuring LFG! remains at the forefront of the Web3 revolution. Your adventure awaits—start it at LFG! For more information, visit: Website: https://www.lfgweb3.app
X: https://x.com/LFGWeb3App
Telegram: https://t.me/LFGOfficialCommunity
Miniapp :https://t.me/lfgweb3official_bot/lfgweb3officialapp?startapp=campaign_press
Discord: https://discord.com/invite/3E3yu9ZQSw
Email: support@lfgweb3.app
Contact Person: Jason Lee This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. We are excited to announce the brand upgrade and a complete UI/UX overhaul of LFG!, transforming it into a cutting-edge Web3 Super App. This evolution marks a new chapter as we become the ultimate platform for education, entertainment, gaming, and trading in the Web3 space. Our mission? To onboard the first billion users into the Web3 ecosystem by offering a seamless, multifaceted experience that brings together the best of Web3 all in one place. Join us on this exciting journey into the future of decentralized tech! Super App as the Traffic Nexus: LFG! aims to expand its content, entertainment, and educational ecosystem centered around the Super App, driving active user engagement for sustained long-term growth and creating a virtuous cycle of traffic and participation. There are three Pillars of the LFG Ecosystem: Learning Hub: Immerse yourself in our wealth of Web3 content that is meticulously curated by AI. Our intelligent algorithms sift through the web to surface the highest-quality resources, tailoring recommendations to align with your interests and preferences. This personalized approach ensures that users are not only empowered with the most relevant and up-to-date industry insights but also provided with the most direct learning pathways, optimizing the learning experience and ensuring that users stay ahead of the curve in the ever-evolving Web3 landscape. Mining Hub: Newcomers are introduced to the Web3 world with an array of mining options intricately linked to future LFG token airdrops. Earn Mining points through community engagement and various platform activities, strategically designed to educate, entertain, and reward users as they explore the Web3 landscape. Our mining experiences, made possible through our strategic partnerships with leading industry exchanges and project owners, offer an experience of the earning process to help users understand Web3. Game Hub: Our platform, a premier destination teeming with an extensive library of games, is committed to enriching the entertainment aspect of Web3 with a variety of engaging and educational game genres, designed to delight and inform players in an interactive setting. We're excited to announce the upcoming release of our gaming SDK, offering project partners unique collaboration opportunities to launch customized LFG games on our platform and attract a diverse array of high-quality games in to LFG via our SDK, further expanding our already robust gaming reserve and solidifying LFG's position as a leading entertainment hub in the Web3 space. Learning Hub: Immerse yourself in our wealth of Web3 content that is meticulously curated by AI. Our intelligent algorithms sift through the web to surface the highest-quality resources, tailoring recommendations to align with your interests and preferences. This personalized approach ensures that users are not only empowered with the most relevant and up-to-date industry insights but also provided with the most direct learning pathways, optimizing the learning experience and ensuring that users stay ahead of the curve in the ever-evolving Web3 landscape. Learning Hub: Immerse yourself in our wealth of Web3 content that is meticulously curated by AI. Our intelligent algorithms sift through the web to surface the highest-quality resources, tailoring recommendations to align with your interests and preferences. This personalized approach ensures that users are not only empowered with the most relevant and up-to-date industry insights but also provided with the most direct learning pathways, optimizing the learning experience and ensuring that users stay ahead of the curve in the ever-evolving Web3 landscape. Mining Hub: Newcomers are introduced to the Web3 world with an array of mining options intricately linked to future LFG token airdrops. Earn Mining points through community engagement and various platform activities, strategically designed to educate, entertain, and reward users as they explore the Web3 landscape. Our mining experiences, made possible through our strategic partnerships with leading industry exchanges and project owners, offer an experience of the earning process to help users understand Web3. Mining Hub: Newcomers are introduced to the Web3 world with an array of mining options intricately linked to future LFG token airdrops. Earn Mining points through community engagement and various platform activities, strategically designed to educate, entertain, and reward users as they explore the Web3 landscape. Our mining experiences, made possible through our strategic partnerships with leading industry exchanges and project owners, offer an experience of the earning process to help users understand Web3. Game Hub: Our platform, a premier destination teeming with an extensive library of games, is committed to enriching the entertainment aspect of Web3 with a variety of engaging and educational game genres, designed to delight and inform players in an interactive setting. We're excited to announce the upcoming release of our gaming SDK, offering project partners unique collaboration opportunities to launch customized LFG games on our platform and attract a diverse array of high-quality games in to LFG via our SDK, further expanding our already robust gaming reserve and solidifying LFG's position as a leading entertainment hub in the Web3 space. Game Hub: Our platform, a premier destination teeming with an extensive library of games, is committed to enriching the entertainment aspect of Web3 with a variety of engaging and educational game genres, designed to delight and inform players in an interactive setting. We're excited to announce the upcoming release of our gaming SDK, offering project partners unique collaboration opportunities to launch customized LFG games on our platform and attract a diverse array of high-quality games in to LFG via our SDK, further expanding our already robust gaming reserve and solidifying LFG's position as a leading entertainment hub in the Web3 space. Key Features of the New LFG! Experience: Create, Learn & Earn: Users can learn from the outstanding content generated on LFG and support their favorite creators by liking and sharing their work, thus creators can earn the rewards by incentives. Explore & Collect: Discover a vast array of Web3 videos and news blogs, and earn rewards while staying updated on industry trends. Social Growth: Invite friends to join LFG! and earn commissions from their in-app activities, fostering a collaborative growth journey in the Web3 space. LFG Points & Token Airdrops: Earn LFG Points that determine your share of future LFG token airdrops, reflecting your active participation in the ecosystem. Create, Learn & Earn: Users can learn from the outstanding content generated on LFG and support their favorite creators by liking and sharing their work, thus creators can earn the rewards by incentives. Explore & Collect: Discover a vast array of Web3 videos and news blogs, and earn rewards while staying updated on industry trends. Social Growth: Invite friends to join LFG! and earn commissions from their in-app activities, fostering a collaborative growth journey in the Web3 space. LFG Points & Token Airdrops: Earn LFG Points that determine your share of future LFG token airdrops, reflecting your active participation in the ecosystem. Exciting New User Engagement Initiatives: Invitation Treasure Hunt: Engage with our community and unlock exclusive opportunities by inviting new members to the LFG platform. For friends you invite, you and your friends will earn a chance to participate in our treasure box lottery, offering a thrilling spin of fortune in your favor. Wheel of Fortune Frenzy: Spin your way to rewards with our Wheel of Fortune event. Users can earn spins by extending their network or by utilizing their hard-earned points, blending the joy of participation with the anticipation of winning. Point Leaderboard Challenge: Compete for supremacy on our point leaderboard and share in the cash bounty. Participate in our daily sprint event from 8 PM to 10 PM for a chance to climb the ranks and claim your share of the prize pool. Plus, leverage your networking prowess to earn invitation points and divide an additional pool of rewards. Gaming Mastery Marathon: Demonstrate your gaming prowess and climb the leaderboards in our mini-games for a chance to share cash rewards and LFG points. Complete designated in-game tasks to earn bonus points, adding an extra layer of reward to your gaming achievements. LFG is committed to fostering a vibrant community that thrives on interaction, competition, and reward. These new initiatives are designed to not only enrich the user experience but also to solidify LFG as a leading platform in the Web3 space. Seamless Integration for a Connected Future: LFG! is more than an app; it's a super app with a super vision for the connected future, seamlessly bridging the gap between Web2 and Web3. Designed as a comprehensive platform for edutainment, LFG! offers a space where users can explore, learn, play, and earn within the dynamic Web3 ecosystem. We are committed to fostering a global shift towards a more inclusive, knowledgeable, and rewarding digital economy. As we expand our ecosystem, LFG! will integrate social networking, signal communities, utility applications and other functional market tools, providing a multi-dimensional user experience that enhances. Become an OG LFG member today to unlock a world of exclusive benefits, airdrop rewards, and a front-row seat to the future of digital interaction. With our innovative approach, LFG! is not just a platform but a catalyst for transformation, setting the stage for a new era of Web3 engagement. Join us on this journey as we continue to innovate, ensuring LFG! remains at the forefront of the Web3 revolution. Your adventure awaits—start it at LFG! For more information, visit: Website: https://www.lfgweb3.app X: https://x.com/LFGWeb3App Telegram: https://t.me/LFGOfficialCommunity Miniapp :https://t.me/lfgweb3official_bot/lfgweb3officialapp?startapp=campaign_press Discord: https://discord.com/invite/3E3yu9ZQSw Email: support@lfgweb3.app Contact Person: Jason Lee Website: https://www.lfgweb3.app https://www.lfgweb3.app X: https://x.com/LFGWeb3App https://x.com/LFGWeb3App https://x.com/LFGWeb3App Telegram: https://t.me/LFGOfficialCommunity https://t.me/LFGOfficialCommunity https://t.me/LFGOfficialCommunity Miniapp :https://t.me/lfgweb3official_bot/lfgweb3officialapp?startapp=campaign_press : https://t.me/lfgweb3official_bot/lfgweb3officialapp?startapp=campaign_ press https://t.me/lfgweb3official_bot/lfgweb3officialapp?startapp=campaign_ Discord: https://discord.com/invite/3E3yu9ZQSw https://discord.com/invite/3E3yu9ZQSw https://discord.com/invite/3E3yu9ZQSw Email: support@lfgweb3.app support@lfgweb3.app support@lfgweb3.app Contact Person: Jason Lee This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here

Introducing LFG!: Your All-in-One Web3 Edutainment Super App

LFG পেশ করছি!: আপনার অল-ইন-ওয়ান ওয়েব3 এডুটেইনমেন্ট সুপার অ্যাপ

Wir präsentieren LFG!: Ihre All-in-One Web3 Edutainment Super App

Presentamos LFG!: su superaplicación educativa y de entretenimiento Web3 todo en uno

Présentation de LFG ! : votre super application Web3 de divertissement éducatif tout-en-un

एलएफजी का परिचय!: आपका ऑल-इन-वन वेब3 एजुटेनमेंट सुपर ऐप

LFG! のご紹介: オールインワンの Web3 エデュテインメント スーパー アプリ

LFG 소개!: 올인원 Web3 Edutainment 슈퍼 앱

Apresentando LFG!: Seu super aplicativo de educação e entretenimento Web3 tudo em um

Представляем LFG!: ваше универсальное Web3-образовательно-развлекательное суперприложение

LFG'yi Tanıtıyoruz!: Hepsi Bir Arada Web3 Eğitim ve Eğlence Süper Uygulamanız

Giới thiệu LFG!: Siêu ứng dụng giải trí giáo dục Web3 toàn diện của bạn

LFG 介绍！：您的一体化 Web3 寓教于乐超级应用程序

Supply Chain - The Achilles Heel of Cybersecurity

Bright Data infrastructure is championed around collecting only publicly available data, backed by an industry-leading know-your-customer (KYC) process and a transparent, acceptable use policy. Rated number one by customers on G2, Bright Data provides two scraping solutions, the Web Scraper API and the Scraping Browser, that simplify the data collection process. This guide covers the following: Advantages of using Bright Data’s scraping tools over traditional methods
Practical demonstration of building a dataset with a scraping browser
Overview of Bright Data’s dataset marketplace for ease of use Let’s get started. Advantages of using Bright Data’s scraping tools The Scraping Browser is one of the industry’s most cutting-edge scraping tools. With this tool, you can run your scripts on fully hosted browsers equipped with a CAPTCHA auto-solver, unlimited scalability, and residential IPs to enhance data collection. According to Bright Data CEO Or Lenchner, the Internet is the world’s largest database; the only issue is organizing its data. Using the Scraping Browser, Bright Data helps you manage your data with its in-built unblocking and hosting properties. Some of the advantages of using Bright Data’s scraping tool includes: Reason #1: Efficiency Bright Data comes embedded with a developer-first dynamic scraping ability with pre-written scripts with different scraping technologies, making it possible to accelerate the data collection process compared to traditional methods. Reason #2: Reliability Data companies trust Bright Data tools for their robustness and stability in handling large-scale data collection across their vast residential IP pool and network, ensuring high-quality and accurate data. Another critical point to note is that Bright Data offers support and resolves issues quickly for its customers around the clock. Reason #3: Global adoption Bright Data powers many global brands, which is interesting to note with Bright Insights. Bright Insights leverages deep technology infrastructure to transform public data into actionable insights that serve more than 20,000+ businesses with crucial public web data. Some notable companies that use Bright Data are Microsoft, Epson, Mozilla, and so on. One widespread use case where companies use Bright Data for their data collection needs is using data for AI. In such a scenario, Bright Data assures its customers and users (data scientists) that they always have AI training data for their machine learning models, providing everything you need from discovering, curating, and collecting web data at scale. The four essential components of the data from Bright Data are: Continuously refreshed


Clean and validated


Compliant and ethical


Scalable and performant Check out the different data types used in AI models to learn more about using datasets to train AI models and LLMs. Practical demonstration of building a dataset with a Scraping Browser According to Bright Data documentation, the Scraping Browser is one of their proxy-unlocking solutions designed to help you focus on your multi-step data collection from browsers while taking care of your full proxy and unblocking infrastructure. Some of the benefits of the Scraping Browser are: Boost developer productivity
Cut infrastructure overheads
Increase success rates
Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium To get started, follow the instructions in the documentation that describes how to use the Scraping Browser. Sign up for free and receive a $5 credit. For a more profound practice on integrating and using Playwright with Python, this guide takes you through scraping public web data. Overview of Bright Data’s dataset marketplace Get fresh datasets from the Bright Data dataset marketplace, a repository of datasets from popular public websites. Browse the dataset category and select the one you want to use. The dataset pricing model is calculated based on the number of records used, whether one-time, biannual, quarterly, or monthly. Advantages of using the marketplace No-code web scraping
Strict validation methods
API for on-demand data Conclusion In this article, we discussed the importance of the Scraping Browser and the readily available pre-built datasets in the marketplace. Before scraping or understanding data from a company dataset, you need not be technical. Streamlining data serves as a means to support companies needing extensive training data and capitalizing on building efficient models. Finally, according to Forbes, the future of web scraping is intricately tied to ML and AI technologies. In 2024, scraping tools will become more intelligent, and the need for manual intervention will diminish. The implications of this can raise concerns about data privacy and ethical use if it scrapes sensitive information from the target site without consent. Are you a developer who wants to try Bright Data and its integration? It supports JavaScript, Node, Python, and programming languages like C#, Java, Go, and Ruby. Learn more Web Scraper APIs Bright Data infrastructure is championed around collecting only publicly available data, backed by an industry-leading know-your-customer (KYC) process and a transparent, acceptable use policy. Rated number one by customers on G2 , Bright Data provides two scraping solutions, the Web Scraper API and the Scraping Browser , that simplify the data collection process. by customers on G2 by customers on G2 Web Scraper API Web Scraper API Scraping Browser Scraping Browser This guide covers the following: Advantages of using Bright Data’s scraping tools over traditional methods Practical demonstration of building a dataset with a scraping browser Overview of Bright Data’s dataset marketplace for ease of use Advantages of using Bright Data’s scraping tools over traditional methods Practical demonstration of building a dataset with a scraping browser Overview of Bright Data’s dataset marketplace for ease of use Let’s get started. Advantages of using Bright Data’s scraping tools Advantages of using Bright Data’s scraping tools The Scraping Browser is one of the industry’s most cutting-edge scraping tools. With this tool, you can run your scripts on fully hosted browsers equipped with a CAPTCHA auto-solver, unlimited scalability, and residential IPs to enhance data collection. According to Bright Data CEO Or Lenchner, the Internet is the world’s largest database; the only issue is organizing its data. Using the Scraping Browser, Bright Data helps you manage your data with its in-built unblocking and hosting properties. Some of the advantages of using Bright Data’s scraping tool includes: Reason #1: Efficiency Reason #1: Efficiency Bright Data comes embedded with a developer-first dynamic scraping ability with pre-written scripts with different scraping technologies, making it possible to accelerate the data collection process compared to traditional methods. Reason #2: Reliability Reason #2: Reliability Data companies trust Bright Data tools for their robustness and stability in handling large-scale data collection across their vast residential IP pool and network, ensuring high-quality and accurate data. Another critical point to note is that Bright Data offers support and resolves issues quickly for its customers around the clock. Reason #3: Global adoption Reason #3: Global adoption Bright Data powers many global brands, which is interesting to note with Bright Insights. Bright Insights leverages deep technology infrastructure to transform public data into actionable insights that serve more than 20,000+ businesses with crucial public web data. Some notable companies that use Bright Data are Microsoft, Epson, Mozilla, and so on. One widespread use case where companies use Bright Data for their data collection needs is using data for AI. In such a scenario, Bright Data assures its customers and users (data scientists) that they always have AI training data for their machine learning models, providing everything you need from discovering, curating, and collecting web data at scale. The four essential components of the data from Bright Data are: Continuously refreshed Clean and validated Compliant and ethical Scalable and performant Continuously refreshed Continuously refreshed Clean and validated Clean and validated Compliant and ethical Compliant and ethical Scalable and performant Scalable and performant Check out the different data types used in AI models to learn more about using datasets to train AI models and LLMs. data types used in AI models data types used in AI models Practical demonstration of building a dataset with a Scraping Browser Practical demonstration of building a dataset with a Scraping Browser According to Bright Data documentation, the Scraping Browser is one of their proxy-unlocking solutions designed to help you focus on your multi-step data collection from browsers while taking care of your full proxy and unblocking infrastructure. Some of the benefits of the Scraping Browser are: Boost developer productivity Cut infrastructure overheads Increase success rates Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium Boost developer productivity Cut infrastructure overheads Increase success rates Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium To get started, follow the instructions in the documentation that describes how to use the Scraping Browser. Sign up for free and receive a $5 credit. documentation documentation Sign up for free Sign up for free $5 For a more profound practice on integrating and using Playwright with Python, this guide takes you through scraping public web data. this guide this guide Overview of Bright Data’s dataset marketplace Overview of Bright Data’s dataset marketplace Get fresh datasets from the Bright Data dataset marketplace , a repository of datasets from popular public websites. Browse the dataset category and select the one you want to use. Bright Data dataset marketplace Bright Data dataset marketplace The dataset pricing model is calculated based on the number of records used, whether one-time, biannual, quarterly, or monthly. Advantages of using the marketplace Advantages of using the marketplace No-code web scraping Strict validation methods API for on-demand data No-code web scraping Strict validation methods API for on-demand data Conclusion Conclusion In this article, we discussed the importance of the Scraping Browser and the readily available pre-built datasets in the marketplace. Before scraping or understanding data from a company dataset, you need not be technical. Streamlining data serves as a means to support companies needing extensive training data and capitalizing on building efficient models. Finally, according to Forbes , the future of web scraping is intricately tied to ML and AI technologies. In 2024, scraping tools will become more intelligent, and the need for manual intervention will diminish. The implications of this can raise concerns about data privacy and ethical use if it scrapes sensitive information from the target site without consent. according to Forbes according to Forbes Are you a developer who wants to try Bright Data and its integration ? It supports JavaScript, Node, Python, and programming languages like C#, Java, Go, and Ruby. try Bright Data and its integration try Bright Data and its integration Learn more Web Scraper APIs Web Scraper APIs Web Scraper APIs Web Scraper APIs

Streamlining AI Data Collection with Bright Data’s Scraping Browser

Why Composable Culture Might Just Be the Future of Everything You Love

কেন কম্পোজেবল সংস্কৃতি আপনার পছন্দের সবকিছুর ভবিষ্যত হতে পারে

Warum die Composable Culture die Zukunft von allem sein könnte, was Sie lieben

Por qué la cultura componible podría ser el futuro de todo lo que amas

Pourquoi la culture composable pourrait bien être l'avenir de tout ce que vous aimez

क्यों कंपोजेबल कल्चर आपकी हर पसंदीदा चीज़ का भविष्य हो सकता है

なぜコンポーザブル文化があなたが愛するすべてのものの未来になるのか

컴포저블 문화가 당신이 사랑하는 모든 것의 미래가 될 수 있는 이유

Por que a cultura combinável pode ser o futuro de tudo que você ama

Почему составная культура может стать будущим всего, что вы любите

Şekillendirilebilir Kültür Neden Sevdiğiniz Her Şeyin Geleceği Olabilir?

Tại sao văn hóa tổng hợp có thể là tương lai của mọi thứ bạn yêu thích

为什么可组合文化可能就是你所热爱的一切事物的未来

Authors:
(1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz;
(2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz;
(3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Table of Links Abstract and 1 Introduction 2 Global convergence 2.1 Quantitative Laplace principle 2.2 Global convergence in mean-field law 3 Numerical experiments and 3.1 One-dimensional illustrative example 3.2 Nonlinear oligopoly games with several goods 4 Conclusion, Acknowledgments, Appendix, and References Abstract In this paper, we present a novel consensus-based zeroth-order algorithm tailored for nonconvex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. Keywords: Non-convex, multiplayer games, Nash equilibrium, swarm optimization, Laplace’s principle. 1 Introduction Multiplayer games [48], ranging from strategic board games to complex economic systems, have always fascinated researchers due to their intricate dynamics and strategic interactions among multiple players. Understanding and analyzing the outcomes of these games is crucial in various fields such as economics [41], social sciences [30], and computer science [22]. In recent years, significant progress has also been made in developing advanced learning approaches within the field of artificial intelligence towards multiplayer scenarios. One notable example is the extension of adversarial learning, which was originally applied to settings with a single generator and discriminator, to accommodate multiple agents [43, 56, 62]. This process can also be observed in reinforcement learning, where multiplayer game theory has been integrated to enhance learning algorithms [8, 19, 42]. The search for global NE in non-convex settings remains an open problem [45, 46]. This challenge arises not only due to the absence of powerful tools compared to those available in convex scenarios but also due to the diversity of non-convex structures, which may require specific methodologies for resolution. While efficient techniques have been developed within convex conditions that have led to significant advancements in multiplayer game models [15, 61], these approaches may prove insufficient when confronted with non-convexity. In such cases, they may become trapped in local NE or approximate solutions while following pseudo-gradients, failing to converge to a global NE. Moreover, while inspiring breakthroughs have been made in solving non-convex two-player min-max games in different contexts, such as Polyak– Lojasiewicz cases [23, 50] or concave cases [44, 52], these advancements may not readily apply to multiplayer settings. This is because the global stationary conditions in multiplayer games are intricately coupled and cannot be independently addressed by each player. Consequently, novel effective methods are required for attaining them, which is exactly the main contribution of this article In this paper we introduce a novel zero-order consensus-based approach for finding global NE points in multiplayer games. This method draws inspiration from the consensus-based optimization (CBO) framework initially introduced in [10,51]. CBO belongs to the family of global optimization methodologies, which leverages systems of interacting particles to achieve consensus around global minimizers of the cost functions. As part of the broader class of metaheuristics [2, 29], CBO orchestrates interactions between local improvement procedures and global strategies, utilizing both deterministic and stochastic processes. This interplay ultimately results in an efficient and robust procedure for exploring the solution space of the cost functions. More importantly, the CBO approach possesses an inherent advantage of being gradient-free. This characteristic makes it particularly desirable when dealing with cost functions that lack smoothness or when computing their derivatives is computationally expensive. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz; (2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz; (3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Authors: Authors: (1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz; (2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz; (3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Global convergence 2 Global convergence 2.1 Quantitative Laplace principle 2.1 Quantitative Laplace principle 2.2 Global convergence in mean-field law 2.2 Global convergence in mean-field law 3 Numerical experiments and 3.1 One-dimensional illustrative example 3 Numerical experiments and 3.1 One-dimensional illustrative example 3.2 Nonlinear oligopoly games with several goods 3.2 Nonlinear oligopoly games with several goods 4 Conclusion, Acknowledgments, Appendix, and References 4 Conclusion, Acknowledgments, Appendix, and References Abstract In this paper, we present a novel consensus-based zeroth-order algorithm tailored for nonconvex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. Keywords: Non-convex, multiplayer games, Nash equilibrium, swarm optimization, Laplace’s principle. Keywords: 1 Introduction Multiplayer games [48], ranging from strategic board games to complex economic systems, have always fascinated researchers due to their intricate dynamics and strategic interactions among multiple players. Understanding and analyzing the outcomes of these games is crucial in various fields such as economics [41], social sciences [30], and computer science [22]. In recent years, significant progress has also been made in developing advanced learning approaches within the field of artificial intelligence towards multiplayer scenarios. One notable example is the extension of adversarial learning, which was originally applied to settings with a single generator and discriminator, to accommodate multiple agents [43, 56, 62]. This process can also be observed in reinforcement learning, where multiplayer game theory has been integrated to enhance learning algorithms [8, 19, 42]. The search for global NE in non-convex settings remains an open problem [45, 46]. This challenge arises not only due to the absence of powerful tools compared to those available in convex scenarios but also due to the diversity of non-convex structures, which may require specific methodologies for resolution. While efficient techniques have been developed within convex conditions that have led to significant advancements in multiplayer game models [15, 61], these approaches may prove insufficient when confronted with non-convexity. In such cases, they may become trapped in local NE or approximate solutions while following pseudo-gradients, failing to converge to a global NE. Moreover, while inspiring breakthroughs have been made in solving non-convex two-player min-max games in different contexts, such as Polyak– Lojasiewicz cases [23, 50] or concave cases [44, 52], these advancements may not readily apply to multiplayer settings. This is because the global stationary conditions in multiplayer games are intricately coupled and cannot be independently addressed by each player. Consequently, novel effective methods are required for attaining them, which is exactly the main contribution of this article In this paper we introduce a novel zero-order consensus-based approach for finding global NE points in multiplayer games. This method draws inspiration from the consensus-based optimization (CBO) framework initially introduced in [10,51]. CBO belongs to the family of global optimization methodologies, which leverages systems of interacting particles to achieve consensus around global minimizers of the cost functions. As part of the broader class of metaheuristics [2, 29], CBO orchestrates interactions between local improvement procedures and global strategies, utilizing both deterministic and stochastic processes. This interplay ultimately results in an efficient and robust procedure for exploring the solution space of the cost functions. More importantly, the CBO approach possesses an inherent advantage of being gradient-free. This characteristic makes it particularly desirable when dealing with cost functions that lack smoothness or when computing their derivatives is computationally expensive. 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

A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Abstract and Introduction

RGB++ Protocol and the Future of Bitcoin: Cipher's Insights from Bitcoin Singapore 2024

RGB++ প্রোটোকল এবং বিটকয়েনের ভবিষ্যৎ: বিটকয়েন সিঙ্গাপুর থেকে সাইফারের অন্তর্দৃষ্টি 2024

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Protocolo RGB++ e o futuro do Bitcoin: Insights da Cipher do Bitcoin Singapura 2024

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RGB++ Protokolü ve Bitcoin'in Geleceği: Bitcoin Singapur 2024'ten Cipher'ın İçgörüleri

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RGB++ 协议和比特币的未来：Cipher 从 2024 年新加坡比特币峰会上获得的见解

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