(Bloomberg) -- Chinese AI chipmaker Cambricon Technologies Corp. soared its 20% daily limit on Monday, leading a sector rally after Bloomberg News reported Beijing was stepping up pressure on domestic companies to ditch Nvidia Corp. processors for local alternatives.Most Read from BloombergA ...

China AI Chip Leader Soars 20% Limit as Beijing Warns on Nvidia

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Authors:
(1) Clemencia Siro, University of Amsterdam, Amsterdam, The Netherlands;
(2) Mohammad Aliannejadi, University of Amsterdam, Amsterdam, The Netherlands;
(3) Maarten de Rijke, University of Amsterdam, Amsterdam, The Netherlands. Authors: Authors: (1) Clemencia Siro, University of Amsterdam, Amsterdam, The Netherlands; (2) Mohammad Aliannejadi, University of Amsterdam, Amsterdam, The Netherlands; (3) Maarten de Rijke, University of Amsterdam, Amsterdam, The Netherlands. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Methodology and 2.1 Experimental data and tasks 2 Methodology and 2.1 Experimental data and tasks 2.2 Automatic generation of diverse dialogue contexts 2.2 Automatic generation of diverse dialogue contexts 2.3 Crowdsource experiments 2.3 Crowdsource experiments 2.4 Experimental conditions 2.4 Experimental conditions 2.5 Participants 2.5 Participants 3 Results and Analysis and 3.1 Data statistics 3 Results and Analysis and 3.1 Data statistics 3.2 RQ1: Effect of varying amount of dialogue context 3.2 RQ1: Effect of varying amount of dialogue context 3.3 RQ2: Effect of automatically generated dialogue context 3.3 RQ2: Effect of automatically generated dialogue context 4 Discussion and Implications 4 Discussion and Implications 5 Related Work 5 Related Work 6 Conclusion, Limitations, and Ethical Considerations 6 Conclusion, Limitations, and Ethical Considerations 7 Acknowledgements and References 7 Acknowledgements and References A. Appendix A. Appendix 3 Results and Analysis We address (RQ1) and (RQ2) by providing an overview of the results and in-depth analysis of our crowdsourcing experiments. We first describe the key data statistics. RQ1 RQ2 3.1 Data statistics Phase 1. Figure 1 presents the distributions of relevance and usefulness ratings across the three variations, C0, C3, and C7. Figure 1a indicates a larger number of dialogues rated as relevant when annotators had no prior context (C0), compared to instances of C3 and C7, where a lower number of dialogues received such ratings. This suggests that in the absence of prior context, annotators are more inclined to perceive the system’s response as relevant, as they lack evidence to assert otherwise. This trend is particularly prevalent when user utterances lean towards casual conversations, such as inquiring about a previously mentioned movie or requesting a similar recommendation to their initial query, aspects to which the annotators have no access. Consequently, this suggests that annotators rely on assumptions regarding the user’s previous inquiries, leading to higher ratings for system response relevance. We observe a similar trend for usefulness (Figure 1b), compared to C3 and C7, C0 has more dialogues rated as useful. The introduction of the user’s next utterance introduced some level of ambiguity to annotators. Evident in instances where the user introduced a new item not mentioned in the system’s response and expressed an intention to watch it, the usefulness of the system’s response became uncertain. This ambiguity arises particularly when annotators lack access to prior context, making it challenging to tell if the movie was mentioned before in the preceding context. These observations highlight the impact of the amount of dialogue context on the annotators’ perceptions of relevance and usefulness in Phase 1. This emphasizes the significance of taking contextual factors into account when evaluating TDSs. Phase 2. In Phase 2, we present findings on how different types of dialogue contexts influence the annotation of relevance and usefulness labels. When the dialogue summary is included as supplementary information for the turn under evaluation (C0-sum), a higher proportion of dialogues are annotated as relevant compared to C0-llm for relevance (60% vs. 52.5%, respectively); see Figure 2a. Phase 2. In contrast to the observations made for relevance, we see in Figure 2b that a higher percentage of dialogues are predominantly labeled as not useful when additional information is provided to the annotators. This accounts for 60% in C0-heu, 47.5% in C0-llm, and 45% in C0-sum. This trend is consistent with our observations from Phase 1, highlighting that while system responses may be relevant, they do not always align with the user’s actual information need. We find that C0-sum exhibits the highest number of dialogues rated as useful, indicating its effectiveness in providing pertinent information to aid annotators in making informed judgments regarding usefulness. 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

Study Finds AI Responses Rated Higher When Context is Limited

London, UK, April 2nd, 2025/Chainwire/--Decentraland's flagship virtual event returns April 9–12, showcasing digital fashion as a new canvas for storytelling, activism, and global identity. Metaverse Fashion Week (MVFW) returns to Decentraland from April 9–12, amid a shifting fashion landscape characterized by declining traditional luxury sales and evolving consumer preferences. Metaverse Fashion Week Metaverse Fashion Week MVFW 2025 highlights digital fashion's growing relevance as a meaningful response to these industry transformations. Under this year's theme, "Infinite Identities," more than 47 global designers and brands leverage digital mediums to challenge creative boundaries and articulate nuanced cultural identities. Hosted on Decentraland 2.0, the platform’s newly redesigned desktop client offers improved visuals, greater interactivity, and better accessibility. These enhancements, developed during the event's hiatus, provide an immersive and engaging environment for attendees and creators alike. Decentraland 2.0 Decentraland 2.0 “This year’s Metaverse Fashion Week comes at a critical moment for fashion and our society at large; one marked by rethinking priorities, authenticity, and connection," says Bay Backner, Head Producer of MVFW. “This year’s Metaverse Fashion Week comes at a critical moment for fashion and our society at large; one marked by rethinking priorities, authenticity, and connection," says Bay Backner, Head Producer of MVFW. “I’ve had the privilege of working closely with every designer and brand taking part, and I’ve seen how digital fashion can elevate unheard voices, ask important questions, and spark real cultural change. In a time like this, when the physical world feels increasingly heavy or limiting, third spaces like Decentraland become places where people can breathe, create, and truly be themselves. MVFW 2025 channels that energy into a live moment for fashion’s next breakout names and creative economies.” “I’ve had the privilege of working closely with every designer and brand taking part, and I’ve seen how digital fashion can elevate unheard voices, ask important questions, and spark real cultural change. In a time like this, when the physical world feels increasingly heavy or limiting, third spaces like Decentraland become places where people can breathe, create, and truly be themselves. MVFW 2025 channels that energy into a live moment for fashion’s next breakout names and creative economies.” Runways: Fashion as Cultural Dialogue Runways have always been the cornerstone of fashion weeks worldwide, but MVFW 2025 reimagines this classic format through immersive, collaborative, and interactive experiences. Attendees can anticipate creative surprises as designers redefine the runway with innovative animated collections that blur the line between digital and physical fashion. Cyberdog: London’s iconic brand brings its neon-infused, rave-culture aesthetic into the virtual realm, blending futuristic fashion with underground electronic subcultures. Free The Youth: Ghanaian collective Free The Youth highlights African streetwear and creativity, celebrating bold, culturally driven designs that bridge physical and virtual worlds. The Banners We Wear: Decentraland’s inaugural MVFW Prize unveils deeply personal collections from emerging designers. With a $15,000 prize pool, this unique competition challenged designers globally to create digital wearables that convey powerful messages about identity, activism, and resilience. Winning entries will debut on a dedicated runway, spotlighting urgent social narratives and elevating voices often marginalized in mainstream fashion. Interactive Designer Showrooms: Digital Fashion as Storytelling At the heart of MVFW 2025 are 16 interactive designer showrooms, each exploring the interplay between fashion, technology, and cultural narrative. These showrooms are divided into two thematic zones, Augmented Self and Storied Self, offering distinct yet complementary perspectives on digital self-expression. Augmented Self: This zone highlights innovative uses of AI, AR, and digital-physical ("phygital") fashion. Showrooms include AI-driven styling by Digital Drip, AR-enhanced virtual fashion by FFFACE.ME, NFT-powered wearable collaborations from Another-1 X Templa, and microchip manicure technology from Chipped. Augmented Self: This zone highlights innovative uses of AI, AR, and digital-physical ("phygital") fashion. Showrooms include AI-driven styling by Digital Drip, AR-enhanced virtual fashion by FFFACE.ME, NFT-powered wearable collaborations from Another-1 X Templa, and microchip manicure technology from Chipped. Storied Self: Dedicated to fashion as cultural storytelling, this zone spotlights global narratives and identity-driven collections. Key highlights include adaptive fashion for prosthetics by Ukraine's SVD Denim, mycelium-inspired digital wearables from Amsterdam's GLTCH Atelier, and culturally resonant digital storytelling from Puerto Rico’s Spatialgineers Collective. Storied Self: Dedicated to fashion as cultural storytelling, this zone spotlights global narratives and identity-driven collections. Key highlights include adaptive fashion for prosthetics by Ukraine's SVD Denim, mycelium-inspired digital wearables from Amsterdam's GLTCH Atelier, and culturally resonant digital storytelling from Puerto Rico’s Spatialgineers Collective. Immersive Experiences & Insightful Conversations Beyond runways and showrooms, MVFW 2025 invites attendees to experience transformative fashion installations and conversations exploring identity, technology, and culture: "Entangled Q" by Cansy: A quantum-inspired art installation featuring avatars that dynamically morph, exploring fluid identities. "Entangled Q" by Cansy: A quantum-inspired art installation featuring avatars that dynamically morph, exploring fluid identities. "OFF-WORLD Galactic Expansion" by The Immersive KIND: A futuristic display merging AI-generated beauty, robotics couture, and interstellar aesthetics. "OFF-WORLD Galactic Expansion" by The Immersive KIND: A futuristic display merging AI-generated beauty, robotics couture, and interstellar aesthetics. A curated series of panels will dive deeper into key conversations, covering the evolving role of avatars in digital self-expression, AI’s impact on fashion creativity, wearable tech in gaming environments, and the intersection of physical and virtual retail. Panelists include industry leaders from Harper’s BAZAAR Australia, Parsons School of Design, Ready Player Me, Institut Français de la Mode, Boy’s Club, and more. Metaverse Fashion Week 2025 taps into a growing appetite for fashion that has meaning beyond trends, signaling a shift toward stories that matter and identities that resonate globally. As digital spaces become essential platforms for expression, Decentraland opens new possibilities for creators and communities to craft a freer, more inclusive vision of fashion. With immersive runways, interactive storytelling, and conversations that challenge the status quo, MVFW 2025 invites everyone to experience how digital fashion can reflect—and reshape—our world. To learn more and get ready for MVFW 2025, users can visit mvfw.org and download the latest client. mvfw.org mvfw.org About Decentraland: Decentraland is the first decentralized, community-driven social virtual world. It empowers users to create, explore, and connect within an open, immersive digital landscape where they fully own their digital assets. Decentraland Decentraland Whether hosting virtual events or designing their digital identity, individuals can shape a world that prioritizes self-expression and freedom—free from the constraints of corporate control. Governed by its community through a decentralized autonomous organization (DAO) and supported by a non-profit Foundation, Decentraland operates as an open-source, traversable world that encourages creativity, ownership, and innovation. By fostering collaboration and community, Decentraland is shaping the future of digital interaction, where individuals can truly own and build their digital lives. Contact Ana Lezama press@decentraland.org This story was distributed as a release by Chainswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Global Designers Challenge Borders, Identity, And The Limits Of Physical Fashion At MVFW 2025

Qlobal Dizaynerlər MFW 2025-də Sərhədlərə, Şəxsiyyətə və Fiziki Moda Sərhədlərinə meydan oxuyurlar

Globalni dizajneri izazivaju granice, identitet i granice fizičke mode na MFW 2025.

Diseñadores globales desafían las fronteras, la identidad y los límites de la moda física en MFW 2025

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グローバルデザイナーがMFW2025で物理ファッションの境界、アイデンティティ、そして限界に挑戦

អ្នករចនាសម្ព័ន្ធទូទាំងពិភពលោកជំរុញកម្រិតខុសគ្នានិងកម្រិតខុសគ្នានៃភាពរចនាសម្ព័ន្ធរចនាសម្ព័ន្ធនៅ MFW 2025 ។

Globālie dizaineri izaicina robežas, identitāti un fiziskās modes robežas MFW 2025

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Designerii globali provoacă granițele, identitatea și limitele modei fizice la MFW 2025

ගෝලීය නිර්මාණකරුවන් MFW 2025 හි භෞතික විලාසිතාගේ සීමාවන්, අනන් යතාව සහ සීමාවන් අභියෝග කරයි

Global Vagadziri Vanodenha Border, Identity, Uye Iyo Miganhu YePhysical Fashoni PaMFW 2025

Global designers challenge border, identity, and the limits of physical fashion at MFW 2025. - Global designers challenge borders, identity, and the limits of physical fashion at MFW 2025

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models 5.1 Limitations Several limitations should be noted. There were insufficient instances in the notes of the emotional support subcategories to evaluate the NLP systems. Emotional support (and lack thereof) is an important and distinct fine-grained category that would ideally be identified in the notes. Second, the RBS was designed with specific lexicons from manual review at MSHS and WCM, may have experienced overfitting and led to an inflated f-score. It would be beneficial to validate these NLP systems on clinical notes from different EHR systems. Other healthcare systems that implement a lexicon-based rules approach will need to perform site-specific template removal to avoid the problem of false-positives. With fine-tuning, the LLM approach may have been able to correctly interpret the templates; however, because the templates were removed from the notes before the annotation process, this was not assessed. 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 Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

NLP Performance in Clinical Notes: Addressing Data Limitations and System Overfitting

Willemstad, Curaçao, Netherlands, March 31st, 2025/GamingWire/--Cloudbet is opening no-limit betting for April's Dota tournament blitz, seizing the moment as the start of the year shapes up to be Dota 2's busiest season outside of The International. With ESL One Raleigh 2025 (April 7-13) and PGL Wallachia Season 4 (April 18-26) both offering million-dollar prize pools on the heels of the recent 7.38c patch, competitive Dota is about to get interesting. This means serious Dota bettors on Cloudbet can now tap into the combined resources of global markets, purpose-built for high-stakes action – meaning serious bettors could potentially win more than the tournaments' champions themselves. Dota bettors on Cloudbet Dota bettors on Cloudbet Cloudbet's no-limit bets now cover five essential Dota markets: Dota markets Dota markets Map Handicap – Allows wagers on matches where underdogs are given an advantage.
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Correct Map Score – Allows predictions of the final map score for a series. Map Handicap – Allows wagers on matches where underdogs are given an advantage. Total Maps – Enables bets on the total number of maps played in a series. Winner – Offers the option to bet on the overall winner of the series. Map Winner – Provides betting on the outcome of individual maps. Correct Map Score – Allows predictions of the final map score for a series. The timing is particularly notable with the 7.38c patch update that dropped during the FISSURE Universe playoffs — shaking up hero rankings and team strategies right in the middle of high-stakes competitions. "We're listening to all the feedback in our esports community about how to bring them closer to games all season long." notes John, the Cloudbet Spokesperson. "And we're doing that with no-limit bets, and integrating many, many more markets. You can bet on which team gets first blood, first barracks, or first Roshan – markets that go beyond the basics but are core to the Dota experience. These are milestone events that make grown adults scream at their monitors, so why not cash in too?” "We're listening to all the feedback in our esports community about how to bring them closer to games all season long." notes John, the Cloudbet Spokesperson. "And we're doing that with no-limit bets, and integrating many, many more markets. You can bet on which team gets first blood, first barracks, or first Roshan – markets that go beyond the basics but are core to the Dota experience. These are milestone events that make grown adults scream at their monitors, so why not cash in too?” New Cloudbet users will also have access to the Welcome Package, featuring up to $2500 in cash, ongoing Rakeback Rewards, and straight cash rewards through the Vault. Once claimed, users enter Cloudbet Rewards, the generous loyalty program designed for consistent bettors. While traditional sportsbooks often limit the size of bets, especially in esports, Cloudbet offering no-limit betting on major Dota 2 markets is an industry first. In high-integrity traditional sports like the NFL and English Premier League, betting limits can reach into the millions. By extending its no-limit betting, already available on major sports, to Dota 2, Cloudbet aims to bring esports into the top tier of the sports betting world. For Dota fans looking to add another dimension to their viewing experience, Cloudbet's no-limit betting offers a way to back predictions with real conviction – and potentially serious returns. For more information, users can visit Cloudbet.com. Cloudbet.com Cloudbet.com ABOUT CLOUDBET Founded in 2013, Cloudbet is the world’s longest-running crypto casino and sportsbook. Over the past decade, players worldwide have placed millions of bets using over 40 different cryptocurrencies. In 2024, Cloudbet introduced the most generous welcome offer and loyalty program online, featuring stacked rewards and guaranteed daily cash drops for frequent bettors. Cloudbet Cloudbet With a wide selection of slots, live casino games, and sports markets—ranging from esports to Premier League and NFL player props—Cloudbet is the leader in secure crypto betting. Users can find more information at Cloudbet.com; Instagram (@cloudbetofficial); Twitter/X (@Cloudbet). Cloudbet.com Cloudbet.com Instagram (@cloudbetofficial) Instagram (@cloudbetofficial) Twitter/X (@Cloudbet) Twitter/X (@Cloudbet) Contact Irene Halcyon Super Holdings B.V. irene@ This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Cloudbet Opens No-limit Betting For April's Dota Tournament Blitz

Cloudbet يفتتح عرض غير محدود لبرنامج Dota Tournament Blitz في أبريل

Cloudbet отваря безлимитни залози за Dota Tournament Blitz през април

Cloudbet eröffnet No-Limit-Wetten für April-Dota-Turnier Blitz

Cloudbet abre apuestas sin límite para el torneo Blitz de Dota de abril

Cloudbet avaa No-limit-vedonlyönnin huhtikuun Dota Tournament Blitzille

Cloudbet、4月のDotaトーナメントブリッツでノーリミットベッティングを開始

Cloudbet открывает безлимитные ставки на апрельский турнир Dota Blitz

අඟහරුවාදා Dota Tournament Blitz සඳහා No-Limit Betting විවෘත කරයි

Cloudbet hap bastet pa kufi për Blitz të Turneut Dota të prillit

ክላውድቤት ንናይ ሚያዝያ ዶታ ውድድር ብሊትዝ ደረት ኣልቦ ውርርድ ከፊቱ

Cloudbet, Nisan Dota Turnuvası Blitz'i İçin Limitsiz Bahis Açıyor

Welcome back to another HackerNoon Startups of the Week! If you’re not already in the loop, each week, the team at HackerNoon showcases a list of startups from our Startups of The Year database. All these startups have been nominated as one of the best in their respective category or region. This week, we’re pleased to bring you FoundersToday, The Forbes Group, and Bloklab. Want to be nominated for HackerNoon’s Startups of The Year? Learn how here. Meet Startups of the Week FoundersToday FoundersToday is a Startup Publication Media that delivers the latest news, insights, and trends in the startup ecosystem. It offers high-quality, unbiased, and thoroughly researched content that covers a wide range of topics, including emerging technologies, venture capital, startup strategies, and entrepreneurial journeys. Located in Linz, Austria, this startup has been nominated as one of the best in their region, along with nominations in the News & Journalism, Publishing, and Writing and Editing categories. Support FoundersToday - Vote here! The Forbes Group The Forbes Group is a full-service consultancy based in Calgary, Alberta. It specializes in aligning businesses with modern value chain strategies that drive sustainability compliance, operational excellence, and measurable growth. Located in Calgary, Canada, this startup has been nominated as one of the best in their region, along with nominations in the Leadership, Professional Services, and Training and Consulting categories. Support The Forbes Group - Vote here! Bloklab Bloklab Oy is Finland's leading technology start-up in blockchain technologies and decentralized finance (DeFi). With a team of experts, Bloklab Oy develops innovative platforms that empower and optimize the digital assets of businesses, adhering to principles of security, transparency, and user-centricity. Located in Helsinki, Finland, this startup has been nominated as one of the best in their region, along with nominations in the Blockchain and Decentralization categories. Support Bloklab - Vote here! Featured Interview of The Week Follow the example of FoundersToday and create your business page on HackerNoon. Having a business profile will allow you to create your very own Evergreen Tech Company News Page and participate in our Tech Companies ranking, weekly and data-driven insights that delve into which tech companies are gaining and losing traction in public awareness. Plus, only with a business page can you answer the awesome interview templates we crafted for you. Today, we’re focusing on the Sao Paulo Startup Interview Template. Sao Paulo was one of our trending startup cities last year, and this edition is no different! If you’re a Sao Paulo-based start up or have insights to share about the tech scene in one of Latin America’s largest cities, this one’s for you! Use this template to showcase your startup’s vision, innovative projects, key lessons, and strategies for overcoming challenges in the city. Insights about how the city evolves and supports entrepreneurs are welcome! Additionally, don’t forget to share how events like HackerNoon’s Startups of the Year have shaped your journey. Highlight why your startup stands out and deserves recognition in 2024. Here’s a quote from FoundersToday featured in their published interview, telling us why they decided to participate in Startups of The Year: We’re excited to participate in HackerNoon’s Startups of the Year because it provides a fantastic platform to share our journey at FoundersToday with a broader tech-savvy audience. HackerNoon’s community is deeply embedded in the startup and entrepreneurial ecosystem, making it an ideal space to highlight our vision, challenges, and successes. By participating, we aim to connect with like-minded entrepreneurs, investors, and tech enthusiasts who can offer valuable insights and collaborations to help us grow further. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template. Here’s how the 2023 nominees did it: Meetanshi, Sniper.xyz, Wallet Factory, Grownu Workforce Management System, Motif, Lightrun Featured Startups Special Package Discover our Lead Generation package, delivering 1M impressions through the HackerNoon Newsletter! With this package, you’ll get: 4 Total Newsletters Sent: 325,000 per send (85% of subscribers reside in the US)

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Your Evergreen Tech Company News Page Learn more about this package here or book a meeting with us! About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of the Year Merch Shophere. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Welcome back to another HackerNoon Startups of the Week! If you’re not already in the loop, each week, the team at HackerNoon showcases a list of startups from our Startups of The Year database . All these startups have been nominated as one of the best in their respective category or region. Startups of The Year database This week, we’re pleased to bring you FoundersToday , The Forbes Group , and Bloklab . FoundersToday The Forbes Group Bloklab Want to be nominated for HackerNoon’s Startups of The Year? Learn how here. Want to be nominated for HackerNoon’s Startups of The Year? Learn how here. Want to be nominated for HackerNoon’s Startups of The Year? Learn how here . here Meet Startups of the Week Meet Startups of the Week FoundersToday FoundersToday FoundersToday FoundersToday is a Startup Publication Media that delivers the latest news, insights, and trends in the startup ecosystem. It offers high-quality, unbiased, and thoroughly researched content that covers a wide range of topics, including emerging technologies, venture capital, startup strategies, and entrepreneurial journeys. Located in Linz, Austria , this startup has been nominated as one of the best in their region, along with nominations in the News & Journalism , Publishing , and Writing and Editing categories. Linz, Austria News & Journalism Publishing Writing and Editing Support FoundersToday - Vote here! Support FoundersToday - Vote here! Support FoundersToday - Vote here ! Vote here The Forbes Group The Forbes Group The Forbes Group The Forbes Group is a full-service consultancy based in Calgary, Alberta. It specializes in aligning businesses with modern value chain strategies that drive sustainability compliance, operational excellence, and measurable growth. Located in Calgary, Canada , this startup has been nominated as one of the best in their region, along with nominations in the Leadership , Professional Services , and Training and Consulting categories. Calgary, Canada Leadership Professional Services Training and Consulting Support The Forbes Group - Vote here! Support The Forbes Group - Vote here! Support The Forbes Group - Vote here ! Vote here Bloklab Bloklab Bloklab Bloklab Oy is Finland's leading technology start-up in blockchain technologies and decentralized finance (DeFi). With a team of experts, Bloklab Oy develops innovative platforms that empower and optimize the digital assets of businesses, adhering to principles of security, transparency, and user-centricity. Located in Helsinki, Finland , this startup has been nominated as one of the best in their region, along with nominations in the Blockchain and Decentralization categories. Helsinki, Finland Blockchain Decentralization Support Bloklab - Vote here! Support Bloklab - Vote here! Support Bloklab - Vote here ! Vote here Featured Interview of The Week Featured Interview of The Week Follow the example of FoundersToday and create your business page on HackerNoon . FoundersToday your business page on HackerNoon Having a business profile will allow you to create your very own Evergreen Tech Company News Page and participate in our Tech Companies ranking , weekly and data-driven insights that delve into which tech companies are gaining and losing traction in public awareness. Plus, only with a business page can you answer the awesome interview templates we crafted for you. Evergreen Tech Company News Page Tech Companies ranking Today, we’re focusing on the Sao Paulo Startup Interview Template . Sao Paulo was one of our trending startup cities last year, and this edition is no different! If you’re a Sao Paulo-based start up or have insights to share about the tech scene in one of Latin America’s largest cities, this one’s for you! Use this template to showcase your startup’s vision, innovative projects, key lessons, and strategies for overcoming challenges in the city. Insights about how the city evolves and supports entrepreneurs are welcome! Additionally, don’t forget to share how events like HackerNoon’s Startups of the Year have shaped your journey. Highlight why your startup stands out and deserves recognition in 2024. Sao Paulo Startup Interview Template trending startup cities Use this template Here’s a quote from FoundersToday featured in their published interview , telling us why they decided to participate in Startups of The Year: FoundersToday published interview We’re excited to participate in HackerNoon’s Startups of the Year because it provides a fantastic platform to share our journey at FoundersToday with a broader tech-savvy audience. HackerNoon’s community is deeply embedded in the startup and entrepreneurial ecosystem, making it an ideal space to highlight our vision, challenges, and successes. By participating, we aim to connect with like-minded entrepreneurs, investors, and tech enthusiasts who can offer valuable insights and collaborations to help us grow further. We’re excited to participate in HackerNoon’s Startups of the Year because it provides a fantastic platform to share our journey at FoundersToday with a broader tech-savvy audience. HackerNoon’s community is deeply embedded in the startup and entrepreneurial ecosystem, making it an ideal space to highlight our vision, challenges, and successes. By participating, we aim to connect with like-minded entrepreneurs, investors, and tech enthusiasts who can offer valuable insights and collaborations to help us grow further. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template. Here’s how the 2023 nominees did it: Meetanshi, Sniper.xyz, Wallet Factory, Grownu Workforce Management System, Motif, Lightrun Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template. Here’s how the 2023 nominees did it: Meetanshi , Sniper.xyz , Wallet Factory , Grownu Workforce Management System , Motif , Lightrun Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template . use this interview template Meetanshi Meetanshi Sniper.xyz Sniper.xyz Wallet Factory Wallet Factory Grownu Workforce Management System Grownu Workforce Management System Motif Motif Lightrun Lightrun Featured Startups Special Package Discover our Lead Generation package, delivering 1M impressions through the HackerNoon Newsletter! Lead Generation With this package, you’ll get: With this package, you’ll get: 4 Total Newsletters Sent: 325,000 per send (85% of subscribers reside in the US)

Open Rate: 13% - 15%
Click-to-Open Rate: 13.61%
Sample: Here Your Evergreen Tech Company News Page 4 Total Newsletters Sent: 325,000 per send (85% of subscribers reside in the US) Open Rate: 13% - 15%
Click-to-Open Rate: 13.61%
Sample: Here Open Rate: 13% - 15% Click-to-Open Rate: 13.61% Sample: Here Open Rate: 13% - 15% Click-to-Open Rate: 13.61% Sample: Here Here Your Evergreen Tech Company News Page Evergreen Tech Company News Page Learn more about this package here or book a meeting with us! Learn more about this package here or book a meeting with us ! here here book a meeting with us book a meeting with us About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview Evergreen Tech Company News Visit our FAQ page to learn more. FAQ Download our design assets here . here Check out the Startups of the Year Merch Shop here . here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform

Meet FoundersToday, Forbes Group, Bloklab Limited: HackerNoon Startups of the Week

Conozca a FoundersToday, Forbes Group y Bloklab Limited: Startups de la semana de HackerNoon

フォーブス・グループ、Blocklab Limited: HackerNoon Startups of the Week

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Susipažinkite su „Forbes Group“ įkūrėjais, „Bloklab Limited“: „HackerNoon“ savaitės startuoliai

Forbes Group, Bloklab Limited, үүсгэн байгуулагчидтай өнөөдөр уулзаарай: Долоо хоногийн шилдэг стартапууд.

Kamachiqkunawan riqsinakuyKunan punchaw, Forbes Grupo, Bloklab Limited: HackerNoon Simana qallariyninkuna

Shirkadda Forbes Group, Bloklab Limited: HackerNoon Startups of the Week

Упознајте ФоундерсТодаи, Форбес Гроуп, Блоклаб Лимитед: ХацкерНоон Стартупс оф тхе Веек

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Forumlar, Forbes Group, Bloklab Limited: HackerNoon Startups of the Week – HackerNoon Group: Forbes Group, Bloklab Limited: HackerNoon Startups of the Week

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2 Related Works 3 Preliminaries 3.1 Fair Supervised Learning and 3.2 Fairness Criteria 3.1 Fair Supervised Learning and 3.2 Fairness Criteria 3.3 Dependence Measures for Fair Supervised Learning 3.3 Dependence Measures for Fair Supervised Learning 4 Inductive Biases of DP-based Fair Supervised Learning 4 Inductive Biases of DP-based Fair Supervised Learning 4.1 Extending the Theoretical Results to Randomized Prediction Rule 4.1 Extending the Theoretical Results to Randomized Prediction Rule 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning 6 Numerical Results 6.1 Experimental Setup 6.1 Experimental Setup 6.2 Inductive Biases of Models trained in DP-based Fair Learning 6.2 Inductive Biases of Models trained in DP-based Fair Learning 6.3 DP-based Fair Classification in Heterogeneous Federated Learning 6.3 DP-based Fair Classification in Heterogeneous Federated Learning 7 Conclusion and References 7 Conclusion and References Appendix A Proofs Appendix A Proofs Appendix B Additional Results for Image Dataset Appendix B Additional Results for Image Dataset 4 Inductive Biases of DP-based Fair Supervised Learning Proof. We defer the proof to the Appendix Proof We remark the difference between the bias levels shown for the DDP case in Theorem 1 and the other dependence metrics in Theorem 2. The bias level for a DDP-based fair leaner could be considerably stronger than that of mutual information, ERMI, and maximal correlation-based fair learners, as the maximum the total variations in Theorem 1 is replaced by their expected value over PS in Theorem 2. S 4.1 Extending the Theoretical Results to Randomized Prediction Rule Proof. We defer the proof to the Appendix. Proof 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors:
(1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk);
(2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk);
(3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk). Authors: Authors: (1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk); (2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk); (3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk).

The Limits of Demographic Parity in AI Models 

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Think-and-Execute 2 Think-and-Execute 3 Experimental Setup 3 Experimental Setup 4 Results 4 Results 5 Analysis 5 Analysis 6 Related Work 6 Related Work 7 Limitations and Discussion 7 Limitations and Discussion 8 Conclusion and References 8 Conclusion and References A Experimental Details A Experimental Details B Details of Think-and-Execute B Details of Think-and-Execute C Prompts Used in Our Experiments C Prompts Used in Our Experiments D Human-written Pseudocode Prompts D Human-written Pseudocode Prompts E Generated Analyses E Generated Analyses F Generated Pseudocode Prompts F Generated Pseudocode Prompts G Qualitative Analysis G Qualitative Analysis 7 Limitations and Discussion A possible limitation of our approach is that we focus on algorithmic reasoning, as we believe it is the best setting to assess LLMs’ capabilities in understanding a complex logic and carrying out a sequence of reasoning step, following the logic. However, we believe that THINK-AND-EXECUTE can be applied to other domains of reasoning that require following a long sequence of reasoning steps, such as multi-hop reasoning (Ji et al., 2020) and symbolic reasoning (Madaan & Yazdanbakhsh, 2022). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Hyungjoo Chae, Yonsei University;
(2) Yeonghyeon Kim, Yonsei University;
(3) Seungone Kim, KAIST AI;
(4) Kai Tzu-iunn Ong, Yonsei University;
(5) Beong-woo Kwak, Yonsei University;
(6) Moohyeon Kim, Yonsei University;
(7) Seonghwan Kim, Yonsei University;
(8) Taeyoon Kwon, Yonsei University;
(9) Jiwan Chung, Yonsei University;
(10) Youngjae Yu, Yonsei University;
(11) Jinyoung Yeo, Yonsei University. Authors: Authors: (1) Hyungjoo Chae, Yonsei University; (2) Yeonghyeon Kim, Yonsei University; (3) Seungone Kim, KAIST AI; (4) Kai Tzu-iunn Ong, Yonsei University; (5) Beong-woo Kwak, Yonsei University; (6) Moohyeon Kim, Yonsei University; (7) Seonghwan Kim, Yonsei University; (8) Taeyoon Kwon, Yonsei University; (9) Jiwan Chung, Yonsei University; (10) Youngjae Yu, Yonsei University; (11) Jinyoung Yeo, Yonsei University.

Think-and-Execute: The Limitations That We Face 

Author:
(1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Author: Author: (1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Basic Definitions and Notations 2.1 Sets 2.1 Sets 2.2 Sequences 2.2 Sequences 2.3 Signatures and Algebras and 2.4 Categories 2.3 Signatures and Algebras and 2.4 Categories 3 Monographs and their Morphisms 3 Monographs and their Morphisms 4 Limits and Colimits 4 Limits and Colimits 5 Drawing Monographs 5 Drawing Monographs 6 Graph Structures and Typed Monographs 6 Graph Structures and Typed Monographs 7 Submonographs and Partial Morphisms 7 Submonographs and Partial Morphisms 8 Algebraic Transformations of Monographs 8 Algebraic Transformations of Monographs 9 Attributed Typed Monographs 9 Attributed Typed Monographs 10 Conclusion and References 10 Conclusion and References 4 Limits and Colimits The colimits of monographs follow the standard constructions of colimits in Sets and Graphs. Sets Graphs Theorem 4.4. The categories of Definition 3.5 are finitely co-complete. Theorem 4.4. The categories of Definition 3.5 are finitely co-complete. We next investigate the limits in categories of monographs. Products of monographs are more difficult to build than products of graphs. This is due to the fact that edges of identical length may be adjacent to edges of different lengths. We therefore see that E (A x B) is only a subset of EA x EB. Lemma 4.6. Every pair of parallel morphisms f, g : A → B has an equalizer (E, e) eq such that E is finite whenever A is finite. Lemma 4.6. Every pair of parallel morphisms f, g : A → B has an equalizer (E, e) eq such that E is finite whenever A is finite. Corollary 4.7. The monomorphisms in Monogr are the injective morphisms. Corollary 4.7. The monomorphisms in Monogr are the injective morphisms. A well-known consequence of Lemmas 4.5 and 4.6 is that all non-empty finite diagrams in Monogr have limits. Since a limit of O-monographs (resp. standard monographs) is an O-monograph (resp. standard), this holds for all categories of Definition 3.5. In particular they all have pullbacks. We shall now investigate the limits of the empty diagram in these categories, i.e., their possible terminal objects. Definition 4.8. For any set of ordinals O, let Definition 4.8. For any set of ordinals O, let Lemma 4.11. Monogr, SMonogr and FMonogr have no terminal object. Lemma 4.11. Monogr, SMonogr and FMonogr have no terminal object. Since terminal objects are limits of empty diagrams obviously these categories are not finitely complete. Theorem 4.12. O-SMonogr is finitely complete for every set of ordinals O. The categories Monogr, SMonogr and FMonogr are not finitely complete. Theorem 4.12. O SMonogr is finitely complete for every set of ordinals O. The categories Monogr SMonogr and FMonogr are not finitely complete. Proof. By Lemmas 4.5, 4.6, 4.9 and 4.11. Proof The category Graphs is also known to be adhesive, a property of pushouts and pullbacks that has important consequences on algebraic transformations (see [8]) and that we shall therefore investigate. Graphs Definition 4.13 (van Kampen squares, adhesive categories). A pushout square (A, B, C, D) is a van Kampen square if for any commutative cube Definition 4.13 A pushout square if for any commutative cube A category has pushouts along monomorphisms if all sources (A, f, g) have pushouts whenever f or g is a monomorphism. A category has pushouts along monomorphisms if all sources (A, f, g) have pushouts whenever f or g is a monomorphism. A category is adhesive if it has pullbacks, pushouts along monomorphisms and all such pushouts are van Kampen squares. A category is adhesive if it has pullbacks, pushouts along monomorphisms and all such pushouts are van Kampen squares. As in the proof that Graphs is adhesive, we will use the fact that the category Sets is adhesive. Graphs Sets Lemma 4.14. E reflects isomorphisms. Lemma 4.14. reflects isomorphisms. A side consequence is that Monogr is balanced, i.e., if f is both a monomorphism and an epimorphism, then by Corollaries 4.3 and 4.7 f is bijective, hence is an isomorphism. More important is that we can use [7, Theorem 24.7], i.e., that a faithful and isomorphism reflecting functor from a category that has some limits or colimits and preserves them, also reflects them. Monogr Lemma 4.15. E preserves and reflects finite colimits. Lemma 4.15. preserves and reflects finite colimits. Proof. It is easy to see from the proofs of Lemmas 4.1 and 4.2 that E preserves both coproducts and coequalizers, so that E preserves all finite co-limits and hence also reflects them. Proof This is particularly true for pushouts. The situation for pullbacks is more complicated since E does not preserve products. Lemma 4.16. E preserves and reflects pullbacks. preserves and reflects pullbacks. Theorem 4.17. The categories of Definition 3.5 are adhesive. Theorem 4.17. The categories of Definition 3.5 are adhesive. Proof. The existence of pullbacks and pushouts is already established. In any of these categories a commutative cube built on a pushout along a monomorphism as bottom face and with pullbacks as back faces, has an underlying cube in Sets that has the same properties by Corollary 4.7, Lemmas 4.15 and 4.16. Since Sets is an adhesive category (see [8]) the underlying bottom face is a van Kampen square, hence such is the bottom face of the initial cube by Lemmas 4.15 and 4.16. Proof Sets Sets 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 Structural Analysis of Limits and Colimits in Monographs 

Being a software engineer who works extensively with financial data, I recently hit a wall with traditional stock market APIs. After getting frustrated with rate limits and expensive subscriptions, I decided to build my own solution using web scraping. Here's how I did it, and what I learned along the way. Introduction: Why I Needed a Different Approach My breaking point came during a personal project where I was trying to analyze market trends. Yahoo Finance's API kept hitting rate limits, and Bloomberg Terminal's pricing made me laugh out loud - there was no way I could justify that cost for a side project. I needed something that would let me: Fetch data without arbitrary limits
Get real-time prices and trading volumes
Access historical data without paying premium fees
Scale up my analysis as needed The Web Scraping Solution After some research and experimentation, I settled on scraping data from two main sources: CNN Money for trending stocks and Yahoo Finance for detailed metrics. Here's how I built it: Setting Up the Basic Infrastructure First, I installed the essential tools: pip install requests bs4 Then I created a basic scraper that could handle network issues gracefully: import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) Grabbing Trending Stocks I started with CNN Money's hot stocks list, which gives me three categories of stocks to track: def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks Getting the Financial Details For each trending stock, I fetch additional data from Yahoo Finance: def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data The Gotchas I Encountered Building this wasn't all smooth sailing. Here are some real issues I hit and how I solved them: Rate Limiting: Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds Data Inconsistencies: Sometimes the scraped data would be malformed. I added validation: def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None Website Changes: The sites occasionally update their HTML structure. I made my selectors more robust: # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) Storing and Using the Data I keep things simple with CSV storage - it's easy to work with and perfect for my needs: import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) What I Learned After running this scraper for several weeks, here are my key takeaways: Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly.
Building in error handling and logging from the start saves huge headaches later.
Stock data is messy - so always validate what you scrape.
Starting simple and iterating works better than trying to build everything at once! What's Next? I'm currently working on adding: News sentiment analysis
Basic pattern recognition
A simple dashboard for visualization Also, would you like to integrate this scraper with machine learning models to predict stock trends? Let me know in the comments! Being a software engineer who works extensively with financial data, I recently hit a wall with traditional stock market APIs. After getting frustrated with rate limits and expensive subscriptions, I decided to build my own solution using web scraping. Here's how I did it, and what I learned along the way. Introduction: Why I Needed a Different Approach My breaking point came during a personal project where I was trying to analyze market trends. Yahoo Finance's API kept hitting rate limits, and Bloomberg Terminal's pricing made me laugh out loud - there was no way I could justify that cost for a side project. I needed something that would let me: Fetch data without arbitrary limits Get real-time prices and trading volumes Access historical data without paying premium fees Scale up my analysis as needed Fetch data without arbitrary limits Get real-time prices and trading volumes Access historical data without paying premium fees Scale up my analysis as needed The Web Scraping Solution After some research and experimentation, I settled on scraping data from two main sources: CNN Money for trending stocks and Yahoo Finance for detailed metrics. Here's how I built it: Setting Up the Basic Infrastructure First, I installed the essential tools: pip install requests bs4 pip install requests bs4 Then I created a basic scraper that could handle network issues gracefully: import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) Grabbing Trending Stocks I started with CNN Money's hot stocks list, which gives me three categories of stocks to track: def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks Getting the Financial Details For each trending stock, I fetch additional data from Yahoo Finance: def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data The Gotchas I Encountered Building this wasn't all smooth sailing. Here are some real issues I hit and how I solved them: Rate Limiting: Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: Rate Limiting : Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: Rate Limiting time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds Data Inconsistencies: Sometimes the scraped data would be malformed. I added validation: Data Inconsistencies : Sometimes the scraped data would be malformed. I added validation: Data Inconsistencies def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None Website Changes: The sites occasionally update their HTML structure. I made my selectors more robust: Website Changes : The sites occasionally update their HTML structure. I made my selectors more robust: Website Changes # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) Storing and Using the Data I keep things simple with CSV storage - it's easy to work with and perfect for my needs: import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) What I Learned After running this scraper for several weeks, here are my key takeaways: After running this scraper for several weeks, here are my key takeaways: Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly. Building in error handling and logging from the start saves huge headaches later. Stock data is messy - so always validate what you scrape. Starting simple and iterating works better than trying to build everything at once! Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly. Building in error handling and logging from the start saves huge headaches later. Stock data is messy - so always validate what you scrape. Starting simple and iterating works better than trying to build everything at once! What's Next? I'm currently working on adding: I'm currently working on adding: News sentiment analysis Basic pattern recognition A simple dashboard for visualization News sentiment analysis Basic pattern recognition A simple dashboard for visualization Also, would you like to integrate this scraper with machine learning models to predict stock trends? Let me know in the comments! machine learning models

How to Build a No-Limits Stock Market Scraper with Python

Table of Links Abstract and I. Introduction II. Background III. Related Works IV. Comparative Analysis V. Findings and Evaluation VI. Conclusion and References V. FINDINGS AND EVALUATION Through this research we’ve observed the inability to successfully implement layer-one sharding on account based transactional models. As demonstrated in section III. B account based systems implement layer-two scaling to emulate the parallelization of UTXO sharding. We also observe the inability for off-chain automation dependent solutions to exist on UTXO based systems, due to smart contract incompatibility. The impact of this lack of solution interoperability between layer one and two presents a glass ceiling of scale out potential. Layer two projects such as Plasma still rely on a heavily constrained main chain for coordination and dispute resolution, zero-knowledge and optimistic rollups all have similar dependencies on parent coordination. If sharding were implemented harmoniously underneath these architectures, bottlenecks would be avoided in main-chain coordination. This allows both scalability layers to scale-out by a factor of the other. Imagine a scenario where a mainchain can handle 10,000 transactions-per-second through off-chain scaling and smart contract automation. The overall network architecture can continually scale on layer-two through nested parent/child relationships, however coordination and resolution cause a mainchain bottleneck. Resolving this with layer-one solutions, such as OmniLedgers linear scale-out sharding would increase potential speeds by a factor of n, where n is equal to mainchain parrelization. To address this we propose the use of an Extended-UTXO model that allows for smart contract compatibility. This specification allows for off-chain automation in rollups, while maintaining the ability to shard UTXO states and transactions at layer-one. A. Design Architecture The eUTXO follows the same graph based approach that the UTXO model does, complementing the concurrent and distributed attributes of blockchains. The graph based model eliminates the need for a globally shared mutable state, which is known to cause highly complex problems when facing computations involving that shared state [6]. The first account model blockchain, Ethereum, did solve how to run expressive smart contracts on a global state machine capable of execution on-chain. The eUTXO is an extension onto the UTXO ledger which notably increases the expressiveness of the model, while still utilizing the concurrent data flows of unspent transactions. In creating a more extensible model, transactions can occur on more expressive state machines without needing to use an account model. Each individual transaction that occurs on a concurrent chain represents steps in state machine and to utilize a more expressive model, the state needs to be maintained and the same contract code is used along the whole sequence of transactions. The eUTXO model maintains contract state by extending unspent transaction outputs from being a pair of validator and digital asset to a now being a treble containing a validator, the asset and a datum, where the datum contains arbitrary contract specific data [6]. The eUTXO model implements local state machines which are capable of running smart contracts on them to produces new unspent transactions as well as maintain the new state to be maintained. This enables developers to build distributed applications on massively scalable platforms. OmniLedger inspired sharding protocols will be adapted to the eUTXO model using verifiable random function (VRF) to accomplish network sharding at layer-one [20]. We intend to use a temporary shard coordinator delegated through VRF election, to generate an unbiased random string. Each epoch the unbiased random string is used in shard assignment. eUTXO transactions will be assigned through the use of a randomly chosen directory service shard. This phase is based on Zilliqa’s approach to transaction sharding [22]. We intend to use the unbiased random string generated by the temporary epoch coordinator to delegate this directory service shard. The directory shard will assign transactions to shards based on the sender’s address and smart contract state being interacted with. Smart contract states will be maintained in the eUTXO transactions respective shard. Contracts will be fully executed globally at the end of each epoch when the directory service coordinator synchronises the entire network state. As the eUTXO model supports the use of smart contracts, further layer 2 solutions such as Zero-Knowledge and Optimistic Rollups could be implemented, and even create nestedchains that communicate through smart contract two-way pegs which could increase throughput exponentially. Authors:
(1) Cayo Fletcher-Smith, Bournemouth University;
(2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background II. Background III. Related Works III. Related Works IV. Comparative Analysis IV. Comparative Analysis V. Findings and Evaluation V. Findings and Evaluation VI. Conclusion and References VI. Conclusion and References V. FINDINGS AND EVALUATION Through this research we’ve observed the inability to successfully implement layer-one sharding on account based transactional models. As demonstrated in section III. B account based systems implement layer-two scaling to emulate the parallelization of UTXO sharding. We also observe the inability for off-chain automation dependent solutions to exist on UTXO based systems, due to smart contract incompatibility. The impact of this lack of solution interoperability between layer one and two presents a glass ceiling of scale out potential. Layer two projects such as Plasma still rely on a heavily constrained main chain for coordination and dispute resolution, zero-knowledge and optimistic rollups all have similar dependencies on parent coordination. If sharding were implemented harmoniously underneath these architectures, bottlenecks would be avoided in main-chain coordination. This allows both scalability layers to scale-out by a factor of the other. Imagine a scenario where a mainchain can handle 10,000 transactions-per-second through off-chain scaling and smart contract automation. The overall network architecture can continually scale on layer-two through nested parent/child relationships, however coordination and resolution cause a mainchain bottleneck. Resolving this with layer-one solutions, such as OmniLedgers linear scale-out sharding would increase potential speeds by a factor of n, where n is equal to mainchain parrelization. To address this we propose the use of an Extended-UTXO model that allows for smart contract compatibility. This specification allows for off-chain automation in rollups, while maintaining the ability to shard UTXO states and transactions at layer-one. A. Design Architecture A. Design Architecture The eUTXO follows the same graph based approach that the UTXO model does, complementing the concurrent and distributed attributes of blockchains. The graph based model eliminates the need for a globally shared mutable state, which is known to cause highly complex problems when facing computations involving that shared state [6]. The first account model blockchain, Ethereum, did solve how to run expressive smart contracts on a global state machine capable of execution on-chain. The eUTXO is an extension onto the UTXO ledger which notably increases the expressiveness of the model, while still utilizing the concurrent data flows of unspent transactions. In creating a more extensible model, transactions can occur on more expressive state machines without needing to use an account model. Each individual transaction that occurs on a concurrent chain represents steps in state machine and to utilize a more expressive model, the state needs to be maintained and the same contract code is used along the whole sequence of transactions. The eUTXO model maintains contract state by extending unspent transaction outputs from being a pair of validator and digital asset to a now being a treble containing a validator, the asset and a datum, where the datum contains arbitrary contract specific data [6]. The eUTXO model implements local state machines which are capable of running smart contracts on them to produces new unspent transactions as well as maintain the new state to be maintained. This enables developers to build distributed applications on massively scalable platforms. OmniLedger inspired sharding protocols will be adapted to the eUTXO model using verifiable random function (VRF) to accomplish network sharding at layer-one [20]. We intend to use a temporary shard coordinator delegated through VRF election, to generate an unbiased random string. Each epoch the unbiased random string is used in shard assignment. eUTXO transactions will be assigned through the use of a randomly chosen directory service shard. This phase is based on Zilliqa’s approach to transaction sharding [22]. We intend to use the unbiased random string generated by the temporary epoch coordinator to delegate this directory service shard. The directory shard will assign transactions to shards based on the sender’s address and smart contract state being interacted with. Smart contract states will be maintained in the eUTXO transactions respective shard. Contracts will be fully executed globally at the end of each epoch when the directory service coordinator synchronises the entire network state. As the eUTXO model supports the use of smart contracts, further layer 2 solutions such as Zero-Knowledge and Optimistic Rollups could be implemented, and even create nestedchains that communicate through smart contract two-way pegs which could increase throughput exponentially. Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. Authors: Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

New Research Reveals How Blockchain Can Scale Beyond Current Limits with eUTXO

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