L Marks is transforming world-leading organisations through integrating pioneering technologies and inspiring an entrepreneurial ethos

L Marks

Authors: (1) Maggie D. Bailey, Colorado School of Mines and National Renewable Energy Lab; (2) Douglas Nychka, Colorado School of Mines; (3) Manajit Sengupta, National Renewable Energy Lab; (4) Aron Habte, National Renewable Energy Lab; (5) Yu Xie, National Renewable Energy Lab; (6) Soutir Bandyopadhyay, Colorado School of Mines. Table of Links Abstract and Intro Data Bayesian Hierarchical Model (BHM) Solar Radiation Example Results Conclusion Appendix A: Simulation Study Appendix B: Regridding Coefficient Estimates References References Accadia, C., Mariani, S., Casaioli, M., Lavagnini, A., and Speranza, A.: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids, Weather and forecasting, 18, 918–932, 2003. Berndt, C. and Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density, Journal of Hydrology: Regional Studies, 15, 184–202, 2018. Chandler, R., Barnes, C., Brierley, C., and Alegre, R.: Regridding and interpolation of climate data for impacts modelling–some cautionary notes, Tech. rep., Copernicus Meetings, 2022. Cressie, N. and Wikle, C. K.: Statistics for Spatio-Temporal Data, 2011. Cressie, N. A.: Change of support and the modifiable areal unit problem, 1996. Diaconescu, E. P., Gachon, P., and Laprise, R.: On the remapping procedure of daily precipitation statistics and indices used in regional climate model evaluation, Journal of Hydrometeorology, 16, 2301–2310, 2015. Ensor, L. A. and Robeson, S. M.: Statistical characteristics of daily precipitation: comparisons of gridded and point datasets, Journal of Applied Meteorology and Climatology, 47, 2468–2476, 2008. Finley, A. O., Banerjee, S., and Gelfand, A. E.: spBayes for large univariate and multivariate point-referenced spatio-temporal data models, arXiv preprint arXiv:1310.8192, 2013. Gelfand, A. E., Zhu, L., and Carlin, B. P.: On the change of support problem for spatio-temporal data, Biostatistics, 2, 31–45, 2001. Handcock, M. S. and Stein, M. L.: A Bayesian analysis of kriging, Technometrics, 35, 403–410, 1993. Harris, T., Li, B., and Sriver, R.: Multi-model Ensemble Analysis with Neural Network Gaussian Processes, arXiv [preprint] arXiv:2202.04152, 2022. Loghmari, I., Timoumi, Y., and Messadi, A.: Performance comparison of two global solar radiation models for spatial interpolation purposes, Renewable and Sustainable Energy Reviews, 82, 837–844, 2018. McGinnis, S. and Mearns, L.: Building a climate service for North America based on the NA-CORDEX data archive, Climate Services, 22, 100 233, 2021. McGinnis, S., Mearns, L., and McDaniel, L.: Effects of Spatial Interpolation Algorithm Choice on Regional Climate Model Data Analysis, "Fall Meeting, American Geophysical Union, San Francisco", 2010. Phillips, D. L. and Marks, D. G.: Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models, Ecological Modelling, 91, 213–229, 1996. Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., and Pomeroy, J. W.: The perils of regridding: examples using a global precipitation dataset, Journal of Applied Meteorology and Climatology, 60, 1561–1573, 2021. Rauscher, S. A., Coppola, E., Piani, C., and Giorgi, F.: Resolution effects on regional climate model simulations of seasonal precipitation over Europe, Climate dynamics, 35, 685–711, 2010. Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., and Shelby, J.: The national solar radiation data base (NSRDB), Renewable and sustainable energy reviews, 89, 51–60, 2018. Whittemore, A. S.: Errors-in-variables regression using Stein estimates, The American Statistician, 43, 226–228, 1989. This paper is under CC 4.0 license. available on arxiv

Regridding Uncertainty for Statistical Downscaling of Solar Radiation: References

Decrypting the Future: Programmable Cryptography And Its Role in Modern Tech

ভবিষ্যতের ডিক্রিপ্টিং: প্রোগ্রামেবল ক্রিপ্টোগ্রাফি এবং আধুনিক প্রযুক্তিতে এর ভূমিকা

Die Zukunft entschlüsseln: Programmierbare Kryptografie und ihre Rolle in der modernen Technologie

Descifrando el futuro: criptografía programable y su papel en la tecnología moderna

Décrypter l'avenir : la cryptographie programmable et son rôle dans la technologie moderne

भविष्य को डिक्रिप्ट करना: प्रोग्रामेबल क्रिप्टोग्राफी और आधुनिक तकनीक में इसकी भूमिका

未来を解読する: プログラム可能な暗号化と現代技術におけるその役割

미래의 암호 해독: 프로그래밍 가능한 암호화와 현대 기술에서의 역할

Descriptografando o futuro: criptografia programável e seu papel na tecnologia moderna

Расшифровка будущего: программируемая криптография и ее роль в современных технологиях

Geleceğin Şifresini Çözmek: Programlanabilir Kriptografi ve Modern Teknolojideki Rolü

Giải mã tương lai: Mật mã có thể lập trình và vai trò của nó trong công nghệ hiện đại

解密未来：可编程加密技术及其在现代技术中的作用

Since its debut, the DYDX token has substantially increased its utility across various critical areas such as Security, Staking, and Governance within the dYdX Chain, establishing itself as a fundamental asset in the ecosystem's infrastructure. As of now, approximately 149 million DYDX tokens, which constitute 14.9% of the total supply, are staked with 60 active Validators. This staking mechanism not only secures the network but also underpins the overall stability and reliability of the dYdX Chain. Rewarding Commitment: Staking Innovations The staking framework has proved beneficial, with over 20 million USDC distributed as staking rewards to more than 18,991 stakeholders who contribute to the network’s security. The governance aspect of the DYDX token has seen significant community engagement, with 55 governance proposals initiated to date. This reflects a robust participation rate and a strong commitment to decentralized decision-making processes. On October 26, 2023, following a favorable community vote, the dYdX Chain officially launched with the DYDX token. This development was a turning point for DYDX, which transitioned from a solely governance-based token to a multi-functional asset. The adoption of a Proof-of-Stake (PoS) model has fortified the dYdX Chain’s security. Staking DYDX tokens to Validators not only secures the network but also decentralizes the control, preventing potential vulnerabilities associated with centralized systems. The innovative staking reward mechanism introduced post-launch allocates 100% of the protocol's fees, mostly in USDC, directly to stakers. This system not only secures the network but also offers stakers practical financial benefits, reflecting a well-thought-out strategy to incentivize and reward community support. The dYdX Chain has generated over $120 billion in trading volume to date, with staking rewards showing notable increases particularly in the early months of 2024. This trend underscores the growing utility and attractiveness of the DYDX staking mechanism. Transitioning from the semi-centralized dYdX v3 on Ethereum to a fully decentralized and independent Layer 1 network marks a significant advancement in the protocol’s governance and operational autonomy. Recent governance changes have made it easier for community members to submit proposals, enhancing accessibility and encouraging more active participation in the network's governance. Bridging ethDYDX to DYDX A substantial portion of ethDYDX tokens has successfully migrated to the dYdX Chain, facilitated by an efficient and user-friendly bridging process. This transition supports the integration of the dYdX ecosystem and enhances its decentralization efforts. According to the latest data from Coingecko, the DYDX token has a total supply of 1 billion, with approximately 50% currently in circulation. This distribution highlights the widespread adoption and active use of DYDX within the dYdX ecosystem. The dYdX Chain continues to evolve, driven by community governance and enhanced by the multifunctional utility of the DYDX token, paving the way for a secure, decentralized, and dynamic future for digital asset trading. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Since its debut, the DYDX token has substantially increased its utility across various critical areas such as Security, Staking, and Governance within the dYdX Chain, establishing itself as a fundamental asset in the ecosystem's infrastructure. As of now, approximately 149 million DYDX tokens, which constitute 14.9% of the total supply, are staked with 60 active Validators. This staking mechanism not only secures the network but also underpins the overall stability and reliability of the dYdX Chain. Rewarding Commitment: Staking Innovations The staking framework has proved beneficial, with over 20 million USDC distributed as staking rewards to more than 18,991 stakeholders who contribute to the network’s security. The governance aspect of the DYDX token has seen significant community engagement, with 55 governance proposals initiated to date. This reflects a robust participation rate and a strong commitment to decentralized decision-making processes. governance aspect On October 26, 2023, following a favorable community vote, the dYdX Chain officially launched with the DYDX token. This development was a turning point for DYDX, which transitioned from a solely governance-based token to a multi-functional asset. The adoption of a Proof-of-Stake (PoS) model has fortified the dYdX Chain’s security. Staking DYDX tokens to Validators not only secures the network but also decentralizes the control, preventing potential vulnerabilities associated with centralized systems. The innovative staking reward mechanism introduced post-launch allocates 100% of the protocol's fees, mostly in USDC, directly to stakers. This system not only secures the network but also offers stakers practical financial benefits, reflecting a well-thought-out strategy to incentivize and reward community support. The dYdX Chain has generated over $120 billion in trading volume to date, with staking rewards showing notable increases particularly in the early months of 2024. This trend underscores the growing utility and attractiveness of the DYDX staking mechanism. Transitioning from the semi-centralized dYdX v3 on Ethereum to a fully decentralized and independent Layer 1 network marks a significant advancement in the protocol’s governance and operational autonomy. Recent governance changes have made it easier for community members to submit proposals, enhancing accessibility and encouraging more active participation in the network's governance. Bridging ethDYDX to DYDX A substantial portion of ethDYDX tokens has successfully migrated to the dYdX Chain, facilitated by an efficient and user-friendly bridging process. This transition supports the integration of the dYdX ecosystem and enhances its decentralization efforts. According to the latest data from Coingecko, the DYDX token has a total supply of 1 billion, with approximately 50% currently in circulation. This distribution highlights the widespread adoption and active use of DYDX within the dYdX ecosystem. latest data The dYdX Chain continues to evolve, driven by community governance and enhanced by the multifunctional utility of the DYDX token, paving the way for a secure, decentralized, and dynamic future for digital asset trading. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Inside the dYdX Chain: Exploring the Future of Cryptocurrency Governance

dYdX চেইনের ভিতরে: ক্রিপ্টোকারেন্সি গভর্নেন্সের ভবিষ্যত অন্বেষণ

Innerhalb der dYdX-Kette: Die Zukunft der Kryptowährungsverwaltung erkunden

Dentro de la cadena dYdX: explorando el futuro de la gobernanza de las criptomonedas

À l'intérieur de la chaîne dYdX : explorer l'avenir de la gouvernance des crypto-monnaies

dYdX चेन के अंदर: क्रिप्टोकरेंसी गवर्नेंस के भविष्य की खोज

dYdXチェーンの内部: 暗号通貨ガバナンスの未来を探る

dYdX 체인 내부: 암호화폐 거버넌스의 미래 탐구

Por dentro da cadeia dYdX: explorando o futuro da governança de criptomoedas

Внутри цепочки dYdX: исследование будущего управления криптовалютами

dydX Zincirinin İçinde: Kripto Para Birimi Yönetişiminin Geleceğini Keşfetmek

Bên trong chuỗi dYdX: Khám phá tương lai của quản trị tiền điện tử

dYdX 链内部：探索加密货币治理的未来

How Etherspot Redefines The Way We Build Ethereum Smart Contracts (Account Abstraction)

ইথারস্পট কীভাবে আমরা ইথেরিয়াম স্মার্ট চুক্তি তৈরি করি তা পুনরায় সংজ্ঞায়িত করে (অ্যাকাউন্ট বিমূর্ততা)

Wie Etherspot die Art und Weise neu definiert, wie wir Ethereum-Smart-Contracts erstellen (Kontoabstraktion)

Cómo Etherspot redefine la forma en que construimos contratos inteligentes de Ethereum (abstracción de cuenta)

Comment Etherspot redéfinit la façon dont nous construisons les contrats intelligents Ethereum (abstraction de compte)

इथरस्पॉट किस तरह से एथेरियम स्मार्ट कॉन्ट्रैक्ट बनाने के तरीके को फिर से परिभाषित करता है (खाता सार)

Etherspot が Ethereum スマート コントラクトの構築方法をどのように再定義するか (アカウントの抽象化)

Etherspot이 Ethereum 스마트 계약 구축 방식을 재정의하는 방법(계정 추상화)

Como o Etherspot redefine a maneira como construímos contratos inteligentes Ethereum (abstração de conta)

Как Etherspot меняет подход к созданию смарт-контрактов Ethereum (абстракция аккаунта)

Etherspot, Ethereum Akıllı Sözleşmeleri Oluşturma Yöntemimizi Nasıl Yeniden Tanımlıyor (Hesap Soyutlaması)

Cách Etherspot xác định lại cách chúng tôi xây dựng hợp đồng thông minh Ethereum (Tóm tắt tài khoản)

Etherspot 如何重新定义我们构建以太坊智能合约的方式（账户抽象）

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 6 Conclusion This work aimed to evaluate the performance of several known heuristic search algorithms, such as D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in terms of solving time, memory consumption, and path cost. The evaluation was made using different randomly generated grid environments with different characteristics alongside personalized grid environments with horizontal walls and different wall lengths as different hinders. Our experimental evaluation revealed that all the algorithms exhibit different performances with strengths and weaknesses under different grid characterizations. D* Lite consistently generated the shortest paths even in obstacle-dense grids, indicating its efficiency in dense environments. ARA* consistently provides faster solutions as the grid size increases, particularly larger than 100, while RTTA* generates faster solutions in smaller grid sizes, with the advantage of not being affected by dense environments. Our study provides valuable insights into selecting the appropriate heuristic search algorithm in the pathfinding domain. Using these insights, we propose a selection algorithm used to optimize the performance needed in a pathfinding domain, such as, solving time, path length, or memory consumption. However, our evaluation focused only on static environments, while dynamic environments may introduce additional challenges. Furthermore, we considered a limited set of experiments, not covering all possible combinations of the grid characteristics. This limitation means that the selection algorithm might not always recommend the most optimal solution, as seen in the example we introduced to evaluate our algorithm. In future work, we aim to address these limitations as follows: Firstly, we plan to extend our evaluation of the search algorithms to include dynamic environments. Secondly, we intend to explore various combinations of both domain characterization and priorities. Furthermore, we want to include additional scenario configuration, extending the random obstacles and the walls scenarios. With such additional understanding, we aim to refine our selection algorithm to automatically take decisions among the best search algorithms, based on the type of obstacles in the local search space. References [1] Nafiz Arica, Aysegul Mut, Alper Yörükçü & Kadir Alpaslan Demir (2017): An Empirical Comparison of Search Approaches for Moving Agents. Comput. Intell. 33(3), pp. 368–400, doi:10.1111/coin.12092. [2] Yngvi Björnsson, Vadim Bulitko & Nathan R. Sturtevant (2009): TBA*: Time-Bounded A*. In Craig Boutilier, editor: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pp. 431–436. Available at http://ijcai.org/ Proceedings/09/Papers/079.pdf. [3] David Bond, Niels A. Widger, Wheeler Ruml & Xiaoxun Sun (2010): Real-Time Search in Dynamic Worlds. In Ariel Felner & Nathan R. Sturtevant, editors: Proceedings of the Third Annual Symposium on Combinatorial Search, SOCS 2010, Stone Mountain, Atlanta, Georgia, USA, July 8 10, 2010, AAAI Press, doi:10.1609/socs.v1i1.18174. Available at http://aaai.org/ocs/index.php/SOCS/SOCS10/paper/ view/2103. [4] Guillaume Brat, Ewen Denney, Dimitra Giannakopoulou, Jeremy Frank & Ari Jónsson (2006): Verification of autonomous systems for space applications. In: 2006 IEEE Aerospace Conference, IEEE, pp. 11–pp, doi:10.1109/AERO.2006.1656029. [5] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest & Clifford Stein (2009): Introduction to Algorithms, 3rd Edition. MIT Press. Available at http://mitpress.mit.edu/books/ introduction-algorithms. [6] A. Elfes (1989): Using occupancy grids for mobile robot perception and navigation. Computer 22(6), pp. 46–57, doi:10.1109/2.30720. [7] Carlos Hernández & Jorge A. Baier (2011): Real-Time Heuristic Search with Depression Avoidance. In Toby Walsh, editor: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, IJCAI/AAAI, pp. 578–583, doi:10.5591/978-1-57735-516- 8/IJCAI11-104. [8] Mohd Javaid, Abid Haleem, Ravi Pratap Singh & Rajiv Suman (2021): Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics 1, pp. 58–75, doi:10.1016/j.cogr.2021.06.001. [9] Sven Koenig (2001): Agent-Centered Search. AI Mag. 22(4), pp. 109–132, doi:10.1609/aimag.v22i4.1596. [10] Sven Koenig (2004): A Comparison of Fast Search Methods for Real-Time Situated Agents, pp. 864–871. doi:10.1109/AAMAS.2004.10122. Available at https://doi.ieeecomputersociety.org/10.1109/ AAMAS.2004.10122. [11] Sven Koenig & Maxim Likhachev (2002): D*Lite, pp. 476–483. Available at http://www.aaai.org/ Library/AAAI/2002/aaai02-072.php. [12] Sven Koenig & Maxim Likhachev (2006): Real-time adaptive A*. In Hideyuki Nakashima, Michael P. Wellman, Gerhard Weiss & Peter Stone, editors: 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 8-12, 2006, ACM, pp. 281–288, doi:10.1145/1160633.1160682. [13] Sven Koenig & Xiaoxun Sun (2009): Comparing real-time and incremental heuristic search for real-time situated agents. Auton. Agents Multi Agent Syst. 18(3), pp. 313–341, doi:10.1007/s10458 008-9061-x. [14] Richard E Korf (1990): Real-time heuristic search. Artificial intelligence 42(2-3), pp. 189–211, doi:10.1016/0004-3702(90)90054-4. [15] Maxim Likhachev, Geoffrey J. Gordon & Sebastian Thrun (2003): ARA*: Anytime A* with Provable Bounds on Sub-Optimality, pp. 767–774. Available at https://proceedings.neurips.cc/paper/2003/hash/ ee8fe9093fbbb687bef15a38facc44d2-Abstract.html. [16] Manuel Müller, Timo Müller, Behrang Ashtari Talkhestani, Philipp Marks, Nasser Jazdi & Michael Weyrich (2021): Industrial autonomous systems: a survey on definitions, characteristics and abilities. atAutomatisierungstechnik 69(1), pp. 3–13, doi:10.1515/auto-2020-0131. [17] Michal Pechoucek, Simon G Thompson & Holger Voos (2008): Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. Springer, doi:10.1007/978-3-7643-8571-2. [18] Francisco J Perez-Grau, J Ramiro Martinez-de Dios, Julio L Paneque, J Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R Astorga & Anibal Ollero (2021): Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems 60, pp. 312–324, doi:10.1016/j.jmsy.2021.06.008. [19] José Ricardo Sánchez-Ibáñez, Carlos Jesús Pérez-del-Pulgar & Alfonso García-Cerezo (2021): Path Planning for Autonomous Mobile Robots: A Review. Sensors 21(23), p. 7898, doi:10.3390/s21237898. [20] Anthony Stentz (1995): The Focussed D* Algorithm for Real-Time Replanning. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Montréal Québec, Canada, August 20-25 1995, 2 Volumes, Morgan Kaufmann, pp. 1652–1659. Available at http://ijcai.org/ Proceedings/95-2/Papers/082.pdf. [21] Dmitry S. Yershov & Steven M. LaValle (2011): Simplicial Dijkstra and A* algorithms for optimal feedback planning. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011, IEEE, pp. 3862–3867, doi:10.1109/IROS.2011.6095032. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 6 Conclusion This work aimed to evaluate the performance of several known heuristic search algorithms, such as D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in terms of solving time, memory consumption, and path cost. The evaluation was made using different randomly generated grid environments with different characteristics alongside personalized grid environments with horizontal walls and different wall lengths as different hinders. Our experimental evaluation revealed that all the algorithms exhibit different performances with strengths and weaknesses under different grid characterizations. D* Lite consistently generated the shortest paths even in obstacle-dense grids, indicating its efficiency in dense environments. ARA* consistently provides faster solutions as the grid size increases, particularly larger than 100, while RTTA* generates faster solutions in smaller grid sizes, with the advantage of not being affected by dense environments. Our study provides valuable insights into selecting the appropriate heuristic search algorithm in the pathfinding domain. Using these insights, we propose a selection algorithm used to optimize the performance needed in a pathfinding domain, such as, solving time, path length, or memory consumption. However, our evaluation focused only on static environments, while dynamic environments may introduce additional challenges. Furthermore, we considered a limited set of experiments, not covering all possible combinations of the grid characteristics. This limitation means that the selection algorithm might not always recommend the most optimal solution, as seen in the example we introduced to evaluate our algorithm. In future work, we aim to address these limitations as follows: Firstly, we plan to extend our evaluation of the search algorithms to include dynamic environments. Secondly, we intend to explore various combinations of both domain characterization and priorities. Furthermore, we want to include additional scenario configuration, extending the random obstacles and the walls scenarios. With such additional understanding, we aim to refine our selection algorithm to automatically take decisions among the best search algorithms, based on the type of obstacles in the local search space. References [1] Nafiz Arica, Aysegul Mut, Alper Yörükçü & Kadir Alpaslan Demir (2017): An Empirical Comparison of Search Approaches for Moving Agents. Comput. Intell. 33(3), pp. 368–400, doi:10.1111/coin.12092. [2] Yngvi Björnsson, Vadim Bulitko & Nathan R. Sturtevant (2009): TBA*: Time-Bounded A*. In Craig Boutilier, editor: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pp. 431–436. Available at http://ijcai.org/ Proceedings/09/Papers/079.pdf. [3] David Bond, Niels A. Widger, Wheeler Ruml & Xiaoxun Sun (2010): Real-Time Search in Dynamic Worlds. In Ariel Felner & Nathan R. Sturtevant, editors: Proceedings of the Third Annual Symposium on Combinatorial Search, SOCS 2010, Stone Mountain, Atlanta, Georgia, USA, July 8 10, 2010, AAAI Press, doi:10.1609/socs.v1i1.18174. Available at http://aaai.org/ocs/index.php/SOCS/SOCS10/paper/ view/2103. [4] Guillaume Brat, Ewen Denney, Dimitra Giannakopoulou, Jeremy Frank & Ari Jónsson (2006): Verification of autonomous systems for space applications. In: 2006 IEEE Aerospace Conference, IEEE, pp. 11–pp, doi:10.1109/AERO.2006.1656029. [5] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest & Clifford Stein (2009): Introduction to Algorithms, 3rd Edition. MIT Press. Available at http://mitpress.mit.edu/books/ introduction-algorithms. [6] A. Elfes (1989): Using occupancy grids for mobile robot perception and navigation. Computer 22(6), pp. 46–57, doi:10.1109/2.30720. [7] Carlos Hernández & Jorge A. Baier (2011): Real-Time Heuristic Search with Depression Avoidance. In Toby Walsh, editor: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, IJCAI/AAAI, pp. 578–583, doi:10.5591/978-1-57735-516- 8/IJCAI11-104. [8] Mohd Javaid, Abid Haleem, Ravi Pratap Singh & Rajiv Suman (2021): Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics 1, pp. 58–75, doi:10.1016/j.cogr.2021.06.001. [9] Sven Koenig (2001): Agent-Centered Search. AI Mag. 22(4), pp. 109–132, doi:10.1609/aimag.v22i4.1596. [10] Sven Koenig (2004): A Comparison of Fast Search Methods for Real-Time Situated Agents, pp. 864–871. doi:10.1109/AAMAS.2004.10122. Available at https://doi.ieeecomputersociety.org/10.1109/ AAMAS.2004.10122. [11] Sven Koenig & Maxim Likhachev (2002): D*Lite, pp. 476–483. Available at http://www.aaai.org/ Library/AAAI/2002/aaai02-072.php. [12] Sven Koenig & Maxim Likhachev (2006): Real-time adaptive A*. In Hideyuki Nakashima, Michael P. Wellman, Gerhard Weiss & Peter Stone, editors: 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 8-12, 2006, ACM, pp. 281–288, doi:10.1145/1160633.1160682. [13] Sven Koenig & Xiaoxun Sun (2009): Comparing real-time and incremental heuristic search for real-time situated agents. Auton. Agents Multi Agent Syst. 18(3), pp. 313–341, doi:10.1007/s10458 008-9061-x. [14] Richard E Korf (1990): Real-time heuristic search. Artificial intelligence 42(2-3), pp. 189–211, doi:10.1016/0004-3702(90)90054-4. [15] Maxim Likhachev, Geoffrey J. Gordon & Sebastian Thrun (2003): ARA*: Anytime A* with Provable Bounds on Sub-Optimality, pp. 767–774. Available at https://proceedings.neurips.cc/paper/2003/hash/ ee8fe9093fbbb687bef15a38facc44d2-Abstract.html. [16] Manuel Müller, Timo Müller, Behrang Ashtari Talkhestani, Philipp Marks, Nasser Jazdi & Michael Weyrich (2021): Industrial autonomous systems: a survey on definitions, characteristics and abilities. atAutomatisierungstechnik 69(1), pp. 3–13, doi:10.1515/auto-2020-0131. [17] Michal Pechoucek, Simon G Thompson & Holger Voos (2008): Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. Springer, doi:10.1007/978-3-7643-8571-2. [18] Francisco J Perez-Grau, J Ramiro Martinez-de Dios, Julio L Paneque, J Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R Astorga & Anibal Ollero (2021): Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems 60, pp. 312–324, doi:10.1016/j.jmsy.2021.06.008. [19] José Ricardo Sánchez-Ibáñez, Carlos Jesús Pérez-del-Pulgar & Alfonso García-Cerezo (2021): Path Planning for Autonomous Mobile Robots: A Review. Sensors 21(23), p. 7898, doi:10.3390/s21237898. [20] Anthony Stentz (1995): The Focussed D* Algorithm for Real-Time Replanning. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Montréal Québec, Canada, August 20-25 1995, 2 Volumes, Morgan Kaufmann, pp. 1652–1659. Available at http://ijcai.org/ Proceedings/95-2/Papers/082.pdf. [21] Dmitry S. Yershov & Steven M. LaValle (2011): Simplicial Dijkstra and A* algorithms for optimal feedback planning. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011, IEEE, pp. 3862–3867, doi:10.1109/IROS.2011.6095032. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Future Directions in Heuristic Search

, the corporation behind the globally known cryptocurrency Shiba Inu, has joined the Content Distribution & Security Association (CDSA). This agreement, announced on April 11, 2024, during the NAB Show in Las Vegas, is a significant milestone for SHIB since it makes it the first Layer 2 (L2) blockchain to cooperate with a major media and entertainment business. The group seeks to harness blockchain technology's unique properties to better content security and delivery in the industry. SHIB SHIB and CDSA's collaboration is more than just a technological project; it is also a response to genuine concerns about content security in the digital era. The advent of deepfakes, plagiarism, and other digital counterfeiting issues has underlined the need for greater safeguards. SHIB's involvement in this partnership reflects the company's larger goal of expanding the utility of its digital assets and solutions, which are based on the Ethereum blockchain, into new areas such as media and entertainment, while also providing novel solutions to long-standing industry challenges. Blockchain Innovations in Media & Entertainment The partnership's main purpose is to research and use blockchain technology's potential to improve content security, authenticity, and distribution. With SHIB's blockchain expertise and CDSA's extensive network, which includes some of the biggest names in media and entertainment, the partnership is poised to pioneer a blockchain-first approach to content management. This includes not just protecting the material, but also creating a more transparent and efficient distribution system that may lessen the risks connected with digital content proliferation. By leveraging blockchain technology, the partnership aims to streamline the process of content distribution and ensure that intellectual property rights are protected. This innovative approach has the potential to revolutionize the way content is managed and distributed in the industry, providing a more secure and efficient system for creators and consumers alike. With the combined expertise of SHIB and CDSA, the partnership is well-positioned to lead the way in shaping the future of content management using blockchain technology. Shiba Inu Lead Developer, Shytoshi Kusama, expressed enthusiasm about the collaboration, stating, “CDSA members are the biggest names in media and entertainment, from the studios to the key partners that are part of the global ecosystem. We look forward to providing a unique and blockchain-first perspective to CDSA’s work in helping media and entertainment executives better utilize these innovative technologies, especially as blockchain and artificial intelligence converge.” This quote underscores the strategic vision behind the partnership, aiming to position blockchain as a cornerstone technology in the media and entertainment industry's future. What this groundbreaking relationship means is very big. Not only does the partnership between SHIB and CDSA make material safer, it also marks a bigger step towards using blockchain technology in everyday life. This move could set a standard for how entertainment companies handle the problems that come with managing digital material, which could lead to more companies using blockchain solutions in this field. This partnership also shows how blockchain and artificial intelligence (AI) are becoming more and more connected, which opens up new ways to make, share, and protect digital material. Final Thoughts and Prospects for the Future The industry is watching closely to see how blockchain technology may redefine content distribution and security paradigms as SHIB and CDSA embark on this collaborative journey at the same time. Beyond the realm of cryptocurrencies, this relationship not only exemplifies SHIB's growing presence in key industries such as media and entertainment, but it also demonstrates blockchain technology's potential to provide concrete solutions to some of the most pressing issues of the digital age. The SHIB-CDSA partnership may usher in a new era of innovation, bringing blockchain technology and artificial intelligence together to create safer, more efficient, and more equitable digital content marketplaces. Don’t forget to like and share the story! This author is an independent contributor publishing via our . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

From Cryptocurrency to Cinema: SHIB's Groundbreaking Deal with CDSA

ক্রিপ্টোকারেন্সি থেকে সিনেমা পর্যন্ত: CDSA-এর সাথে SHIB-এর গ্রাউন্ডব্রেকিং চুক্তি

Von der Kryptowährung zum Kino: SHIBs bahnbrechender Deal mit CDSA

De las criptomonedas al cine: el acuerdo innovador de SHIB con CDSA

De la crypto-monnaie au cinéma : l'accord révolutionnaire de SHIB avec CDSA

क्रिप्टोकरेंसी से सिनेमा तक: SHIB का CDSA के साथ अभूतपूर्व समझौता

暗号通貨から映画まで: SHIB と CDSA の画期的な契約

암호화폐에서 영화까지: SHIB의 CDSA와의 획기적인 거래

Da criptomoeda ao cinema: o acordo inovador da SHIB com a CDSA

От криптовалюты к кино: революционная сделка SHIB с CDSA

Kripto Paradan Sinemaya: SHIB'in CDSA ile Çığır Açan Anlaşması

Từ tiền điện tử đến điện ảnh: Thỏa thuận đột phá của SHIB với CDSA

从加密货币到电影：SHIB 与 CDSA 的开创性交易

In a crucial step towards decentralizing the artificial intelligence (AI) and large language models (LLM) arena, Masa Network has launched its artificial intelligence (AI) data network and marketplace. Masa is at the forefront of a digital revolution, allowing consumers to contribute to the advancement of artificial intelligence. With a startling flood of over 1.4 million users contributing around 40 million anonymized data points, Masa provides individuals with the opportunity to earn. The platform that Masa has created is a demonstration of the strength that comes from communal effort. It has been made available to users all over the world, allowing them to not only manage their digital footprint but also monetize it via the training of artificial intelligence models. Because of the creative method that Masa takes, users can receive token rewards by sharing their data and underutilized computational power. These rewards can be earned via a variety of activities, such as completing quests inside the Masa app or browsing the web with the Masa Chrome Browser Extension. Masa's Zero-Knowledge Soulbound Tokens (zkSBT) are the core of the company's invention. These tokens ensure that the data of users is kept secret and encrypted. This revolutionary technology gives users the ability to keep complete control over their data while also allowing them to share it in the Masa Data Marketplace on their terms in exchange for token payments. A New Era for AI Developers Masa gives developers several possibilities. An abundance of high-quality validated personal data enables the creation of specialized AI models. These models, customized to today's complex digital environment, might lead to highly personalized artificial intelligence agents and assistants, changing how we use technology. Masa's decentralized LLMs simplify development by avoiding model hosting issues. This speeds up artificial intelligence development and makes cutting-edge technology more accessible to developers from diverse backgrounds. Co-founder Calanthia Mei encapsulates the essence of Masa’s ambition: "Our mission is to radically transform AI, by empowering users to earn from powering the AI economy." This vision isn’t confined to the realms of Web3; Masa is setting its sights on revolutionizing the entire AI industry by bridging the gap with Web2 developers and institutions. The introduction of the MASA token marks a significant milestone in Masa's journey. A record-breaking Coinlist Community Sale that was six times oversubscribed and generated $8.75 million in under 17 minutes demonstrated the currency's widespread interest. It is now accessible on numerous blockchain networks. The Road Ahead: Masa's Strategic Milestones Masa's trajectory is further supported by its exceptional fundraising efforts, which resulted in $18 million from top investors and acceptance into prestigious accelerators like as Binance MVB and Hashkey AI. With over 48,000 node operators and a dedicated user base, Masa is more than just a platform; it is a flourishing ecosystem poised to reshape the AI economy. Masa has set aside 25 million MASA tokens for trading contests and AI data farming, and 6.6 million for marketplace data sharing to celebrate its mainnet debut. This purposeful strategy aims to increase involvement and build a dynamic, active community around Masa's platform. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. In a crucial step towards decentralizing the artificial intelligence (AI) and large language models (LLM) arena, Masa Network has launched its artificial intelligence (AI) data network and marketplace. Masa is at the forefront of a digital revolution, allowing consumers to contribute to the advancement of artificial intelligence. With a startling flood of over 1.4 million users contributing around 40 million anonymized data points, Masa provides individuals with the opportunity to earn. decentralizing artificial intelligence Masa Network The platform that Masa has created is a demonstration of the strength that comes from communal effort. It has been made available to users all over the world, allowing them to not only manage their digital footprint but also monetize it via the training of artificial intelligence models. Because of the creative method that Masa takes, users can receive token rewards by sharing their data and underutilized computational power. These rewards can be earned via a variety of activities, such as completing quests inside the Masa app or browsing the web with the Masa Chrome Browser Extension. Masa's Zero-Knowledge Soulbound Tokens (zkSBT) are the core of the company's invention. These tokens ensure that the data of users is kept secret and encrypted. This revolutionary technology gives users the ability to keep complete control over their data while also allowing them to share it in the Masa Data Marketplace on their terms in exchange for token payments. A New Era for AI Developers Masa gives developers several possibilities. An abundance of high-quality validated personal data enables the creation of specialized AI models. These models, customized to today's complex digital environment, might lead to highly personalized artificial intelligence agents and assistants, changing how we use technology. Masa's decentralized LLMs simplify development by avoiding model hosting issues. This speeds up artificial intelligence development and makes cutting-edge technology more accessible to developers from diverse backgrounds. Co-founder Calanthia Mei encapsulates the essence of Masa’s ambition: "Our mission is to radically transform AI, by empowering users to earn from powering the AI economy." This vision isn’t confined to the realms of Web3; Masa is setting its sights on revolutionizing the entire AI industry by bridging the gap with Web2 developers and institutions. The introduction of the MASA token marks a significant milestone in Masa's journey. A record-breaking Coinlist Community Sale that was six times oversubscribed and generated $8.75 million in under 17 minutes demonstrated the currency's widespread interest. It is now accessible on numerous blockchain networks. The Road Ahead: Masa's Strategic Milestones Masa's trajectory is further supported by its exceptional fundraising efforts, which resulted in $18 million from top investors and acceptance into prestigious accelerators like as Binance MVB and Hashkey AI. With over 48,000 node operators and a dedicated user base, Masa is more than just a platform; it is a flourishing ecosystem poised to reshape the AI economy. Masa has set aside 25 million MASA tokens for trading contests and AI data farming, and 6.6 million for marketplace data sharing to celebrate its mainnet debut. This purposeful strategy aims to increase involvement and build a dynamic, active community around Masa's platform. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Masa Network's AI Marketplace Goes Live: A Game-Changer for Data Monetization

মাসা নেটওয়ার্কের এআই মার্কেটপ্লেস লাইভ হয়: ডেটা মনিটাইজেশনের জন্য একটি গেম-চেঞ্জার

Der KI-Marktplatz von Masa Network geht live: Ein Wendepunkt für die Datenmonetarisierung

El mercado de IA de Masa Network se pone en marcha: un punto de inflexión para la monetización de datos

Le marché de l'IA de Masa Network est lancé : un changement de donne pour la monétisation des données

मासा नेटवर्क का एआई मार्केटप्लेस लाइव हुआ: डेटा मुद्रीकरण के लिए एक गेम-चेंजर

Masa Network の AI マーケットプレイスが始動: データ収益化のゲームチェンジャー

Masa Network의 AI 마켓플레이스 출시: 데이터 수익화의 판도를 바꾸는 변화

O mercado de IA da Masa Network entra em operação: uma virada de jogo para a monetização de dados

Рынок искусственного интеллекта Masa Network запускается: меняет правила игры в монетизации данных

Masa Network'ün Yapay Zeka Pazaryeri Yayına Başlıyor: Veriden Para Kazanmada Oyun Değiştirici

Thị trường AI của Masa Network đi vào hoạt động: Công cụ thay đổi cuộc chơi để kiếm tiền từ dữ liệu

Masa Network 的 AI 市场上线：数据货币化的游戏规则改变者

said that it has secured more than $8 million in investment to expand the capabilities of the Solana blockchain platform. The investment, which came from a combination of top venture capital companies and angel investors, demonstrates the IT community's belief in Zeus Network's mission. Notable funders include Mechanism Capital, OKX Ventures, and individuals such as Solana co-founder Anatoly Yakovenko. Zesus Network 's ambitious attempt to link multiple Web3 ecosystems via the Solana Virtual Machine (SVM) and its proprietary Zeus Layer will benefit from this financial backing, which is intended to boost the project. Zeus Network The Zeus Layer seeks to provide seamless and secure data transmission between major blockchain platforms such as Bitcoin, Ethereum, and BNB. This endeavour, which has the potential to drastically change the DeFi ecosystem landscape, exemplifies Zeus Network's unique approach. Zeus Network has fully embraced the power of community, as seen by their massive Discord and Twitter followings. The Jupiter community picked Zeus Network as the best project for its token launch on the Jupiter LFG Launchpad, emphasising the project's community focus. These types of interactions are critical for fostering a supportive community around Zeus Network's products. The introduction of APOLLO, Zeus Network's first dApp, demonstrates the network's potential to encourage innovation in the DeFi field by bridging liquidity gaps between Bitcoin and Solana. The introduction of the $ZEUS token on the Jupiter LFG Launchpad on April 4th marks an important milestone for Zeus Network and its community. With a 3% total supply allocation for the $ZEUS airdrop to Jupiter voters and other stakeholders, the effort demonstrates how decentralised governance concepts are being adopted and rewarded. The Future Landscape of Blockchain Interoperability is at the forefront of making blockchains operate together, and they want Solana to be the core of the whole blockchain ecosystem. The network will rely on SVM and Zeus Layer nodes to offer robust security and fast data transport. This will improve the DeFi area's connectivity and accessibility. Zeus Network This initiative promises to boost liquidity, simplify difficult programmes, and make decentralised finance more accessible to everybody. As Zeus Network expands, it will be critical to monitor how it influences the Solana ecosystem and determines the future of blockchain interoperability. By emphasising interoperability, Zeus Network aims to transform how various blockchains communicate and collaborate. Solana's function as a central hub is strengthened by making data trading simple and secure, while also opening up new prospects for decentralised banking. As Zeus Network grows and evolves, it will have a significant impact on how blockchains may collaborate and on the Solana ecosystem. This will have a significant impact on the future of DeFi and ensure that everyone has access to financial services. Final Thoughts The recent funding achievement by marks a pivotal step towards realizing its vision of a more interconnected blockchain ecosystem. With strong community support and the backing of prominent investors, Zeus Network is well-positioned to drive forward the integration and innovation within the DeFi space. As the blockchain landscape continues to evolve, Zeus Network’s contributions could significantly influence the direction of decentralized finance and blockchain interoperability. Zeus Network Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Zeus Network’s $8 Million Funding: A New Era for Solana’s Blockchain Ecosystem

জিউস নেটওয়ার্কের $8 মিলিয়ন তহবিল: সোলানার ব্লকচেইন ইকোসিস্টেমের জন্য একটি নতুন যুগ

8 Millionen US-Dollar Finanzierung für Zeus Network: Eine neue Ära für das Blockchain-Ökosystem von Solana

Financiamiento de $ 8 millones de Zeus Network: una nueva era para el ecosistema blockchain de Solana

Financement de 8 millions de dollars de Zeus Network : une nouvelle ère pour l'écosystème blockchain de Solana

ज़ीउस नेटवर्क की $8 मिलियन की फंडिंग: सोलाना के ब्लॉकचेन इकोसिस्टम के लिए एक नया युग

Zeus Network の 800 万ドルの資金調達: Solana のブロックチェーン エコシステムの新時代

Zeus Network의 800만 달러 자금 조달: 솔라나 블록체인 생태계의 새로운 시대

Financiamento de US$ 8 milhões da Zeus Network: uma nova era para o ecossistema Blockchain de Solana

Финансирование Zeus Network в размере 8 миллионов долларов: новая эра для блокчейн-экосистемы Solana

Zeus Network'ün 8 Milyon Dolarlık Finansmanı: Solana'nın Blockchain Ekosistemi İçin Yeni Bir Dönem

Khoản tài trợ trị giá 8 triệu USD của Zeus Network: Kỷ nguyên mới cho hệ sinh thái Blockchain của Solana

Zeus Network 获 800 万美元融资：Solana 区块链生态系统的新时代

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