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Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Matteco


IDEALERS B2B PVT LTD


Terrenity


HIJAU (c/o. PT Investasi Hijau Selaras)


Undesert Corporation


Waterly


Felicidad


Tracco


seeng LTD


Tree++ (Tree Plus Plus)


LEAP for Climate


Aatral ESP


ÁCARA CLIMATE


RenTech Global Solutions


VALKYRIE SOLAR SOLUTIONS


Alder Green Solutions


H2next


Eurasia Natural History Association


Climate Change Writers


Global Cerah


Tasteme


Virya Energy


Zetin Solar Technology Kenya


R.Flo


Ocergy


CENmat


Achelous Pure Metal Co. Ltd.


GoNetZero


Silogic Technology


Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st, and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. 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 Shop 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. 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. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech , Emerging Tech - October 9, 2024 Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ Startups of The Year ” award, featuring the most promising and innovative companies in the Startups of The Year startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Startups in the Climate Tech industry nominated Startups in the Climate Tech industry Startups in the Climate Tech industry Matteco IDEALERS B2B PVT LTD Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Waterly Felicidad Tracco seeng LTD Tree++ (Tree Plus Plus) LEAP for Climate Aatral ESP ÁCARA CLIMATE RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS Alder Green Solutions H2next Eurasia Natural History Association Climate Change Writers Global Cerah Tasteme Virya Energy Zetin Solar Technology Kenya R.Flo Ocergy CENmat Achelous Pure Metal Co. Ltd. GoNetZero Silogic Technology Gaia First Matteco Matteco IDEALERS B2B PVT LTD IDEALERS B2B PVT LTD Terrenity Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Undesert Corporation Waterly Waterly Felicidad Felicidad Tracco Tracco seeng LTD seeng LTD Tree++ (Tree Plus Plus) Tree++ (Tree Plus Plus) LEAP for Climate LEAP for Climate Aatral ESP Aatral ESP ÁCARA CLIMATE ÁCARA CLIMATE RenTech Global Solutions RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS VALKYRIE SOLAR SOLUTIONS Alder Green Solutions Alder Green Solutions H2next H2next Eurasia Natural History Association Eurasia Natural History Association Climate Change Writers Climate Change Writers Global Cerah Global Cerah Tasteme Tasteme Virya Energy Virya Energy Zetin Solar Technology Kenya Zetin Solar Technology Kenya R.Flo R.Flo Ocergy Ocergy CENmat CENmat Achelous Pure Metal Co. Ltd. Achelous Pure Metal Co. Ltd. GoNetZero GoNetZero Silogic Technology Silogic Technology Gaia First Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st , and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Startups of The Year 2024 nominate their favorite startups until October 31st nominate their favorite startups Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. get free interviews startup-friendly packages startup-friendly packages Evergreen Tech Company News Evergreen Tech Company News In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here 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 Startups of The Year 2024 many stories many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview on HackerNoon Evergreen Tech Company News Evergreen Tech Company News Visit our FAQ page to learn more. FAQ FAQ Download our design assets here . here here Check out the Startups of The Year Merch Shop here . 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 startup-friendly packages Meet our sponsors: 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 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 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 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 AI end-to-end Search and Discovery Platform

Startups of The Year 2024: 3,246 Startups Nominated in the Climate Tech Industry

የ2024 የአመቱ ጅምር፡ 3,246 ጅምር በአየር ንብረት ቴክ ኢንዱስትሪ ውስጥ ተመርጠዋል።

Стартапы 2024 года: 3246 стартапаў, намінаваных у індустрыі кліматычных тэхналогій

Startupy roku 2024: 3 246 nominovaných startupů v odvětví Climate Tech

Startups del año 2024: 3246 startups nominadas en la industria de la tecnología climática

Startups do ano 2024: 3.246 startups nomeadas na industria da tecnoloxía climática

Startupovi godine 2024.: 3246 startupova nominiranih u industriji klimatskih tehnologija

Startup dell'anno 2024: 3.246 startup nominate nel settore Climate Tech

2024年のスタートアップ・オブ・ザ・イヤー：気候技術業界で3,246社のスタートアップがノミネート

წლის სტარტაპები 2024: 3246 სტარტაპი ნომინირებულია კლიმატის ტექნოლოგიების ინდუსტრიაში

2024 жылдың стартаптары: климаттық технологиялар индустриясында 3 246 стартап ұсынылды

Startups of the Year 2024: 3 246 startupov nominovaných v odvetví klimatických technológií

Các công ty khởi nghiệp của năm 2024: 3.246 công ty khởi nghiệp được đề cử trong ngành công nghệ khí hậu

BUIDLing Your Next Great AI dApp? These 9 'Ethereum Alternatives' Should Be on Your Radar.

আপনার পরবর্তী দুর্দান্ত AI dApp তৈরি করছেন? এই 9টি 'ইথেরিয়াম বিকল্প' আপনার রাডারে থাকা উচিত।

Sie bauen Ihre nächste großartige KI-dApp? Diese 9 „Ethereum-Alternativen“ sollten Sie auf dem Schirm haben.

¿Está desarrollando su próxima gran aplicación de inteligencia artificial descentralizada? Estas 9 "alternativas a Ethereum" deberían estar en su radar.

Vous êtes en train de créer votre prochaine grande dApp IA ? Ces 9 « alternatives à Ethereum » devraient figurer sur votre radar.

अपना अगला बढ़िया AI dApp बनाना चाहते हैं? ये 9 'एथेरियम विकल्प' आपके रडार पर होने चाहिए।

次の素晴らしい AI dApp を構築していますか? 注目すべき 9 つの「Ethereum の代替」。

다음의 훌륭한 AI dApp을 구축하고 계신가요? 이 9가지 '이더리움 대안'이 여러분의 레이더에 있어야 합니다.

CONSTRUINDO seu próximo grande dApp de IA? Essas 9 'Alternativas ao Ethereum' devem estar no seu radar.

СОЗДАЕТЕ свое следующее великолепное dApp на базе искусственного интеллекта? Эти 9 «альтернатив Ethereum» должны быть на вашем радаре.

Bir Sonraki Harika Yapay Zeka dApp'inizi mi İnşa Ediyorsunuz? Radarınızda Olması Gereken Bu 9 'Ethereum Alternatifi'

XÂY DỰNG DApp AI tuyệt vời tiếp theo của bạn? 9 'Giải pháp thay thế Ethereum' này nên nằm trong tầm ngắm của bạn.

正在构建您的下一个伟大的 AI dApp？这 9 个“以太坊替代品”应该引起您的关注。

Unlocking the Secrets of Autocatalytic Sets: How Bit Strings and Reactions Shape Molecular Evolution

Never Underestimate Logs When It Comes To System Security

The Human Roots of Rising Fascism

দ্য হিউম্যান রুটস অফ রাইজিং ফ্যাসিজম

Die menschlichen Wurzeln des aufkommenden Faschismus

Las raíces humanas del fascismo en ascenso

Les racines humaines du fascisme montant

उभरते फासीवाद की मानवीय जड़ें

ファシズムの台頭の人間的ルーツ

떠오르는 파시즘의 인간 뿌리

As raízes humanas do fascismo em ascensão

Человеческие корни растущего фашизма

Yükselen Faşizmin İnsani Kökleri

Nguồn gốc con người của chủ nghĩa phát xít đang trỗi dậy

法西斯主义兴起的人性根源

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References 6 Conclusion In this paper, we present CGAAL, our model checker of alternating-time temporal logic properties in concurrent games. CGAAL checks such properties by encoding the problem as an extended dependency graph and then computes the satisfaction relation using the distributed local on-the-fly CERTAINZERO algorithm by Dalsgaard et al. [4]. We provide multiple novel search strategies for the algorithm and allow concurrent games to be expressed in our language LCGS. Our experiments show that the local on the-fly algorithm outperforms the global algorithm in the majority of cases. We also find that CGAAL outperforms the state-of-the-art tool PRISM-games by being up to two orders of magnitude faster, especially in models where synchronisations affect the internal state of modules and whenever we are not required to compute the entire fixed point. However, this comparison is unfair, since our test only uses a fraction of PRISM-games feature set. CGAAL is still in early development and much work is needed before it competes with PRISM in terms of capabilities. However, dependency graphs have been used for encoding various model checking problems, and we intend to incorporate these techniques into CGAAL. References [1] Rajeev Alur, Thomas A. Henzinger & Orna Kupferman (2002): Alternating-Time Temporal Logic. J. ACM 49(5), p. 672–713, doi:10.1145/585265.585270. [2] Edmund M. Clarke & E. Allen Emerson (1982): Design and synthesis of synchronization skeletons using branching time temporal logic. In Dexter Kozen, editor: Logics of Programs, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52–71, doi:10.1007/BFb0025774. [3] Edmund M Clarke, Thomas A Henzinger, Helmut Veith, Roderick Bloem et al. (2018): Handbook of model checking. 10, Springer, doi:10.1007/978-3-319-10575-8. [4] Dalsgaard, Andreas E. and Enevoldsen, Søren and Fogh, Peter and Jensen, Lasse S. and Jepsen, Tobias S. and Kaufmann, Isabella and Larsen, Kim G. and Nielsen, Søren M. and Olesen, Mads Chr. and Pastva, Samuel and Srba, Jiˇrí (2017): Extended Dependency Graphs and Efficient Distributed Fixed-Point Computation. In Wil van der Aalst & Eike Best, editors: Application and Theory of Petri Nets and Concurrency, Springer International Publishing, Cham, pp. 139–158, doi:10.1007/978-3-319-57861-3_10. [5] Søren Enevoldsen, Mathias Claus Jensen, Kim Guldstrand Larsen, Anders Mariegaard & Jiˇrí Srba (2020): Verification of Multiplayer Stochastic Games via Abstract Dependency Graphs. LOPSTR2020, doi:10.1007/978-3-030-68446-4_13. [6] Søren Enevoldsen, Kim Guldstrand Larsen & Jirí Srba (2022): Extended abstract dependency graphs. Int. J. Softw. Tools Technol. Transf. 24(1), pp. 49–65, doi:10.1007/s10009-021-00638-8. [7] Glen Jeh & Jennifer Widom (2002): SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada, ACM, pp. 538–543, doi:10.1145/775047.775126. [8] Jonas F. Jensen, Thomas Nielsen, Lars K. Oestergaard & Jiˇrí Srba (2016): TAPAAL and Reachability Analysis of P/T Nets, pp. 307–318. Springer Berlin Heidelberg, Berlin, Heidelberg, doi:10.1007/978-3-662-53401-4_16. [9] Jonas Finnemann Jensen, Kim Guldstrand Larsen, Jiri Srba & Lars Kaerlund Østergaard (2016): Efficient model-checking of weighted CTL with upper-bound constraints. International Journal on Software Tools for Technology Transfer 18(4), pp. 409–426, doi:10.1007/s10009-014-0359-5. [10] Mathias Claus Jensen, Anders Mariegaard & Kim Guldstrand Larsen (2019): Symbolic model checking of weighted PCTL using dependency graphs. NASA Formal Methods Symposium, pp. 298–315, doi:10.1007/978-3-030-20652-9_20. [11] M. Kwiatkowska, G. Norman & D. Parker (2011): PRISM 4.0: Verification of Probabilistic Real-time Systems. In G. Gopalakrishnan & S. Qadeer, editors: Proc. 23rd International Conference on Computer Aided Verification (CAV’11), LNCS 6806, Springer, pp. 585–591, doi:10.1007/978-3-642-22110-1_47. [12] M. Kwiatkowska, G. Norman, D. Parker & G. Santos (2020): PRISM-games 3.0: Stochastic Game Verification with Concurrency, Equilibria and Time. In: Proc. 32nd International Conference on Computer Aided Verification (CAV’20), LNCS 12225, Springer, pp. 475–487, doi:10.1007/978-3-030-53291-8_25. [13] Sangkeun Lee, Minsuk Kahng & Sang goo Lee (2015): Constructing compact and effective graphs for recommender systems via node and edge aggregations. Expert Systems with Applications 42(7), pp. 3396–3409, doi:10.1016/j.eswa.2014.11.062. [14] Xinxin Liu & Scott A. Smolka (1998): Simple Linear-Time Algorithms for Minimal Fixed Points (Extended Abstract). In Kim Guldstrand Larsen, Sven Skyum & Glynn Winskel, editors: Automata, Languages and Programming, 25th International Colloquium, ICALP’98, Aalborg, Denmark, July 13-17, 1998, Proceedings, Lecture Notes in Computer Science 1443, Springer, pp. 53–66, doi:10.1007/BFb0055040. [15] Lawrence Page, Sergey Brin, Rajeev Motwani & Terry Winograd (1998): The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Library Technologies Project. Available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1768. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References 6 Conclusion In this paper, we present CGAAL, our model checker of alternating-time temporal logic properties in concurrent games. CGAAL checks such properties by encoding the problem as an extended dependency graph and then computes the satisfaction relation using the distributed local on-the-fly CERTAINZERO algorithm by Dalsgaard et al. [4]. We provide multiple novel search strategies for the algorithm and allow concurrent games to be expressed in our language LCGS. Our experiments show that the local on the-fly algorithm outperforms the global algorithm in the majority of cases. We also find that CGAAL outperforms the state-of-the-art tool PRISM-games by being up to two orders of magnitude faster, especially in models where synchronisations affect the internal state of modules and whenever we are not required to compute the entire fixed point. However, this comparison is unfair, since our test only uses a fraction of PRISM-games feature set. CGAAL is still in early development and much work is needed before it competes with PRISM in terms of capabilities. However, dependency graphs have been used for encoding various model checking problems, and we intend to incorporate these techniques into CGAAL. References [1] Rajeev Alur, Thomas A. Henzinger & Orna Kupferman (2002): Alternating-Time Temporal Logic. J. ACM 49(5), p. 672–713, doi:10.1145/585265.585270. [2] Edmund M. Clarke & E. Allen Emerson (1982): Design and synthesis of synchronization skeletons using branching time temporal logic. In Dexter Kozen, editor: Logics of Programs, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52–71, doi:10.1007/BFb0025774. [3] Edmund M Clarke, Thomas A Henzinger, Helmut Veith, Roderick Bloem et al. (2018): Handbook of model checking. 10, Springer, doi:10.1007/978-3-319-10575-8. [4] Dalsgaard, Andreas E. and Enevoldsen, Søren and Fogh, Peter and Jensen, Lasse S. and Jepsen, Tobias S. and Kaufmann, Isabella and Larsen, Kim G. and Nielsen, Søren M. and Olesen, Mads Chr. and Pastva, Samuel and Srba, Jiˇrí (2017): Extended Dependency Graphs and Efficient Distributed Fixed-Point Computation. In Wil van der Aalst & Eike Best, editors: Application and Theory of Petri Nets and Concurrency, Springer International Publishing, Cham, pp. 139–158, doi:10.1007/978-3-319-57861-3_10. [5] Søren Enevoldsen, Mathias Claus Jensen, Kim Guldstrand Larsen, Anders Mariegaard & Jiˇrí Srba (2020): Verification of Multiplayer Stochastic Games via Abstract Dependency Graphs. LOPSTR2020, doi:10.1007/978-3-030-68446-4_13. [6] Søren Enevoldsen, Kim Guldstrand Larsen & Jirí Srba (2022): Extended abstract dependency graphs. Int. J. Softw. Tools Technol. Transf. 24(1), pp. 49–65, doi:10.1007/s10009-021-00638-8. [7] Glen Jeh & Jennifer Widom (2002): SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada, ACM, pp. 538–543, doi:10.1145/775047.775126. [8] Jonas F. Jensen, Thomas Nielsen, Lars K. Oestergaard & Jiˇrí Srba (2016): TAPAAL and Reachability Analysis of P/T Nets, pp. 307–318. Springer Berlin Heidelberg, Berlin, Heidelberg, doi:10.1007/978-3-662-53401-4_16. [9] Jonas Finnemann Jensen, Kim Guldstrand Larsen, Jiri Srba & Lars Kaerlund Østergaard (2016): Efficient model-checking of weighted CTL with upper-bound constraints. International Journal on Software Tools for Technology Transfer 18(4), pp. 409–426, doi:10.1007/s10009-014-0359-5. [10] Mathias Claus Jensen, Anders Mariegaard & Kim Guldstrand Larsen (2019): Symbolic model checking of weighted PCTL using dependency graphs. NASA Formal Methods Symposium, pp. 298–315, doi:10.1007/978-3-030-20652-9_20. [11] M. Kwiatkowska, G. Norman & D. Parker (2011): PRISM 4.0: Verification of Probabilistic Real-time Systems. In G. Gopalakrishnan & S. Qadeer, editors: Proc. 23rd International Conference on Computer Aided Verification (CAV’11), LNCS 6806, Springer, pp. 585–591, doi:10.1007/978-3-642-22110-1_47. [12] M. Kwiatkowska, G. Norman, D. Parker & G. Santos (2020): PRISM-games 3.0: Stochastic Game Verification with Concurrency, Equilibria and Time. In: Proc. 32nd International Conference on Computer Aided Verification (CAV’20), LNCS 12225, Springer, pp. 475–487, doi:10.1007/978-3-030-53291-8_25. [13] Sangkeun Lee, Minsuk Kahng & Sang goo Lee (2015): Constructing compact and effective graphs for recommender systems via node and edge aggregations. Expert Systems with Applications 42(7), pp. 3396–3409, doi:10.1016/j.eswa.2014.11.062. [14] Xinxin Liu & Scott A. Smolka (1998): Simple Linear-Time Algorithms for Minimal Fixed Points (Extended Abstract). In Kim Guldstrand Larsen, Sven Skyum & Glynn Winskel, editors: Automata, Languages and Programming, 25th International Colloquium, ICALP’98, Aalborg, Denmark, July 13-17, 1998, Proceedings, Lecture Notes in Computer Science 1443, Springer, pp. 53–66, doi:10.1007/BFb0055040. [15] Lawrence Page, Sergey Brin, Rajeev Motwani & Terry Winograd (1998): The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Library Technologies Project. Available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1768. This paper is available on Arxiv under CC 4.0 license. Arxiv

CGAAL: Wrapping It Up and What Comes Next

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. Table of Links Abstract & Introduction Abstraction and Reasoning Corpus (ARC) Related Work Object-centric Models for ARC Grids MDL-based Model Learning Evaluation Conclusion and Perspectives, References & Supplementary Materials 3 Related Work The ARC benchmark is recent and not many approaches have been published so far. All those we know define a DSL (Domain-Specific Language) of programs that transform an input grid into an output grid, and search for a program that is correct on the training examples [10,3,23,2]. The differences mostly lie in the primitive transformations (prior knowledge) and in the search strategy. It is tempting to define more and more primitives like the Kaggle winner did, hence more prior knowledge, but this means a less intelligent system according to Chollet’s measure. To guide the search in the huge program space, those approaches use either grammatical evolution [10], neural networks [3], search tree pruning with hashing and Tabu list [23], or stochastic search trained on solved tasks [2]. A difficulty is that the output grids are generally only used to score a candidate program so that the search is kind of blind. Alford [3] improves this with a neural-guided bi-directional search that grows the program in both directions, from input and output. Xu [23] compares the in-progress generated grid to the expected grid but this limits the approach to the tasks whose output grids have the same size and same objects as the input grids. DSL-based approaches have a scaling issue because the search space increases exponentially with the number of primitives. Ainooson [2] alleviates this difficulty by defining high-level primitives that embody specialized search strategies. We compare and discuss their performance in the evaluation section. Johnson et al. [14] report on a psychological study of ARC. It reveals that humans use object-centric mental representations to solve ARC tasks. This is in contrast with existing solutions that are based on grid transformations. Interestingly, the tasks that are found the most difficult by humans are those based on logics (e.g., an exclusive-or between grids) and symmetries (e.g., rotation), precisely those most easily solved by DSL-based approaches. The study exhibits two challenges: (1) the need for a large set of primitives, especially about geometry; (2) the difficulty to identify objects, which can be only visible in part due to overlap or occlusion. A valuable resource is LARC, for Language-annotated ARC [1], collected by crowd-sourcing. It provides for most training tasks one or several natural programs that confirm the object-centric and declarative nature of human representations. A natural program is short textual descriptions produced by a participant that could be used by another participant to generate test output grids (without access to the training examples). Beyond the ARC benchmark, a number of work has been done in the domain of program synthesis, which is also known as program induction or programming by examples (PbE) [17]. An early approach is Inductive Logic Programming (ILP) [19], where target predicates are learned from symbolic representations. PbE is used in the FlashFill feature of Microsoft Excel 2013 to learn complex string processing formulas from a few examples [18]. Dreamcoder [8] alternates a wake phase that uses a neurally guided search to solve tasks, and a sleep phase that extends a library of abstractions to compress programs found during wake. Bayesian program learning was shown to outperform deep learning at parsing and generating handwritten world’s alphabets [16]. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. Table of Links Abstract & Introduction Abstract & Introduction Abstraction and Reasoning Corpus (ARC) Abstraction and Reasoning Corpus (ARC) Related Work Related Work Object-centric Models for ARC Grids Object-centric Models for ARC Grids MDL-based Model Learning MDL-based Model Learning Evaluation Evaluation Conclusion and Perspectives, References & Supplementary Materials Conclusion and Perspectives, References & Supplementary Materials 3 Related Work The ARC benchmark is recent and not many approaches have been published so far. All those we know define a DSL (Domain-Specific Language) of programs that transform an input grid into an output grid, and search for a program that is correct on the training examples [10,3,23,2]. The differences mostly lie in the primitive transformations (prior knowledge) and in the search strategy. It is tempting to define more and more primitives like the Kaggle winner did, hence more prior knowledge, but this means a less intelligent system according to Chollet’s measure. To guide the search in the huge program space, those approaches use either grammatical evolution [10], neural networks [3], search tree pruning with hashing and Tabu list [23], or stochastic search trained on solved tasks [2]. A difficulty is that the output grids are generally only used to score a candidate program so that the search is kind of blind. Alford [3] improves this with a neural-guided bi-directional search that grows the program in both directions, from input and output. Xu [23] compares the in-progress generated grid to the expected grid but this limits the approach to the tasks whose output grids have the same size and same objects as the input grids. DSL-based approaches have a scaling issue because the search space increases exponentially with the number of primitives. Ainooson [2] alleviates this difficulty by defining high-level primitives that embody specialized search strategies. We compare and discuss their performance in the evaluation section. Johnson et al. [14] report on a psychological study of ARC. It reveals that humans use object-centric mental representations to solve ARC tasks. This is in contrast with existing solutions that are based on grid transformations. Interestingly, the tasks that are found the most difficult by humans are those based on logics (e.g., an exclusive-or between grids) and symmetries (e.g., rotation), precisely those most easily solved by DSL-based approaches. The study exhibits two challenges: (1) the need for a large set of primitives, especially about geometry; (2) the difficulty to identify objects, which can be only visible in part due to overlap or occlusion. A valuable resource is LARC, for Language-annotated ARC [1], collected by crowd-sourcing. It provides for most training tasks one or several natural programs that confirm the object-centric and declarative nature of human representations. A natural program is short textual descriptions produced by a participant that could be used by another participant to generate test output grids (without access to the training examples). Beyond the ARC benchmark, a number of work has been done in the domain of program synthesis, which is also known as program induction or programming by examples (PbE) [17]. An early approach is Inductive Logic Programming (ILP) [19], where target predicates are learned from symbolic representations. PbE is used in the FlashFill feature of Microsoft Excel 2013 to learn complex string processing formulas from a few examples [18]. Dreamcoder [8] alternates a wake phase that uses a neurally guided search to solve tasks, and a sleep phase that extends a library of abstractions to compress programs found during wake. Bayesian program learning was shown to outperform deep learning at parsing and generating handwritten world’s alphabets [16]. This paper is available on Arxiv under CC 4.0 license. Arxiv

The ARC Benchmark: Related Work You Should Know About

I remember back when mobile devices started to gain momentum and popularity. While I was excited for a way to stay in touch with friends and family, I was far less excited about being placed on call length minutes and the number of text messages I could utilize … before being forced to pay more. limits Believe it or not, the #646 (#MIN) and #674 (#MSG) contact entries were still lingering in my address book until a recent clean-up effort. At one time, those numbers provided a handy mechanism to determine how close I was to hitting the monthly limits enforced by my service provider. Along some very similar lines, I recently found myself in an interesting position as a software engineer – figuring out how to log less to avoid exceeding log ingestion limits set by our observability platform provider. I began to wonder how much longer this paradigm was going to last. The Toil of Evaluating Logs for Ingestion I remember the first time my project team was contacted because log ingestion thresholds were exceeding the expected limit with our observability partner. A collection of new RESTful services had recently been deployed in order to replace an aging monolith. From a supportability perspective, our team had made a conscious effort to provide the production support team with a great deal of logging – in the event the services did not perform as expected. There were more edge cases than there were regression test coverage, so we were expecting alternative flows to trigger results that would require additional debugging if they did not process as expected. Like most cases, the project had aggressive deadlines that could not be missed. When we were instructed to “log less” an unplanned effort became our priority. The problem was, we weren’t 100% certain how best to proceed. We didn’t know what components were in a better state of validation (to have their logs reduced), and we weren’t exactly sure how much logging we would need to remove to no longer exceed the threshold. To our team, this effort was a great example of what has become known as toil: “Toil is the kind of work that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and that scales linearly as a service grows.” – Eric Harvieux (Google Site Reliability Engineering) Every minute our team spent on reducing the amount of logs ingested into the observability platform came at the expense of delivering fewer features and functionality for our services. After all, this was our first of many planned releases. Seeking a “Log Whatever You Feel Necessary” Approach What our team really needed was a scenario where our observability partner was fully invested in the success of our project. In this case, it would translate to a “log whatever you feel necessary” approach. Those who have walked this path before will likely be thinking “this is where JV has finally lost his mind.” Stay with me here as I think I am on to something big. Unfortunately, the current expectation is that the observability platform place limits on the amount of logs that can be ingested. The sad part of this approach is that, in doing so, observability platforms put their needs of their customers – who are relying on and paying for their services. can ahead This is really no different from a time when I relied on the #MIN and #MSG contacts in my phone to make sure I lived within the limits placed on me by my mobile service provider. Eventually, my mobile carrier removed those limits, allowing me to use their services in a manner that made me successful. The bottom line here is that consumers leveraging observability platforms should be able to ingest whatever they feel is important to support their customers, products, and services. It’s up to the observability platforms to accommodate the associated challenges as customers desire to ingest more. This is just like how we engineer our services in a demand-driven world. I cannot imagine telling my customer, “Sorry, but you’ve given us too much to process this month.” Pay For Your Demand – Not Ingestion The better approach here is the concept of paying for insights and not limiting the actual log ingestion. After all, this is 2024 – a time when we all should be used to handling massive quantities of data. The “pay for your demand – not ingestion” concept has been considered a “miss” in the observability industry… until recently, when I read that Sumo Logic has disrupted the DevSecOps world by . This market-disruptor approach embraces the concept of “log whatever you feel necessary” with a north star focused on eliminating silos of log data that were either disabled or skipped due to ingestion thresholds. removing limits on log ingestion Once ingested, AI/ML algorithms help identify and diagnose issues – even before they surface as incidents and service interruptions. Sumo Logic is taking on the burden of supporting additional data because they realize that customers are willing to pay a fair price for the insights gained from their approach. So what does this new strategy to observability cost expectations look like? It can be difficult to pinpoint exactly, but as an example, if your small-to-medium organization is producing an average of 25 MB of log data for ingestion per hour, this could translate into an immediate 10-20% savings ( ) on your observability bill. using Sumo Logic’s price estimator In taking this approach, every single log is available in a custom-built platform that scales along with an entity’s observability growth. As a result, AI/ML features can draw upon this information instantly to help diagnose problems – even before they surface with consumers. When I think about the project I mentioned above, I truly believe both my team and the production support team would have been able to detect anomalies faster than what we were forced to implement. Instead, we had to react to unexpected incidents that impacted the customer’s experience. Conclusion I was able to delete the #MIN and #MSG entries from my address book because my mobile provider eliminated those limits, providing a better experience for me, their customer. My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” – J. Vester In 2023, I also started thinking hard about toil and making a conscious effort to look for ways to avoid or eliminate this annoying productivity-killer. The concept of “zero dollar ingest” has disrupted the observability market by taking a lead from the mobile service provider's playbook. By eliminating log ingestion thresholds, it puts customers in a better position to be successful with their own customers, products, and services (learn more about Sumo Logic’s project ). here From my perspective, not only does this adhere to my mission statement, it provides a toil-free solution to the problem of log ingestion, data volume, and scale. Have a really great day!

DevSecOps: It’s Time To Pay For Your Demand – Not Ingestion

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII VIII. CONCLUSION We presented a categorical approach to the composition method, and specifically Feferman–Vaught–Mostowski theorems. We exploit game comonads to encapsulate the logics and their model comparison games. Surprising connections to classical constructions in category theory, and especially the monad theory of bilinear maps emerged from this approach, cf. Section V-A. For finite model theorists, our work provides a novel highlevel account of many FVM theorems, abstracting away from individual logics and constructions. Furthermore, concrete instances of these theorems are verified purely semantically, by finding a suitable collection homomorphisms forming a Kleisli law satisfying (S2’), instead of the usual delicate verification that strategies of model comparison games compose. For game comonads, we provide a much needed tool that enables us to handle logical relationships between structures as they are transformed or viewed in terms of different logics. The FVM results in this paper, combined with judicious use of the techniques described in Section VII can be pushed significantly further. FVM theorems and the compositional method are of particular significance in the setting of monadic second-order (MSO) logic [49]. Exploiting the results we have presented for the composition method, a follow up paper will give a comonadic semantics for MSO, and develop a semantic account of Courcelle’s algorithmic meta-theorems [5]. The original theorem of Feferman–Vaught [3] is very flexible, including incorporating structure on the indices of families of models to be combined by an operation, as well as the models themselves. This aspect is currently outside the scope of our comonadic methods. An ad-hoc adaptation of our approach also gives a proof of the FVM theorem for free amalgamations, but this does not follow directly from our theorems. Developing these extensions is left to future work. The present work bears some resemblance to Turi and Plotkin’s bialgebraic semantics [50], a categorical model of structural operational semantics (SOS) [51]. Turi and Plotkin noticed that the SOS rules assigning behaviour to syntax could be abstracted as a certain distributive law λ. An algebra encodes the composition operations of the syntax, and a coalgebra the behaviour. If this pair is suitably compatible with λ, forming a so-called λ-bialgebra, then crucially bisimulation is a congruence with respect to the composition operations. Both bialgebraic semantics and our work presented in this paper give a categorical account of well-behaved composition operations, with the interaction between composition and observable behaviour mediated by some form of distributive law. There are also essential differences: bialgebraic semantics typically focuses on assigning behaviour to syntax, whereas FVM theorems encompass operations on models. Our FVM theorems are parametric in a choice of logic or observable behaviour, while the notion of bisimulation in bialgebraic semantics is fixed by the coalgebra signature functor. The natural presence of positive existential and counting quantifier variants of FVM theorems, and the incorporation of resource parameters, do not seem to have a direct analogue in bialgebraic semantics. The similarities are intriguing, and exploring the relationship between bialgebraic semantics and our approach to FVM theorems is left to future work. We would like to thank Clemens Berger for suggesting the connection with parametric adjoints and Nathanael Arkor for his suggestions on terminology. We are also grateful to the members of the EPSRC project “Resources and co-resources” for their feedback. Acknowledgements: REFERENCES [1] L. Libkin, Elements of finite model theory. Springer, 2004, vol. 41. [2] A. Mostowski, “On direct products of theories,” The Journal of Symbolic Logic, vol. 17, no. 1, pp. 1–31, 1952. [3] S. Feferman and R. L. Vaught, “The first-order properties of algebraic systems,” Fundamenta Mathematicae, vol. 47, 1959. [4] J. A. 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