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One of the announcements by stated that omniverse has successfully come out of the beta phase and is available for download by individuals. Initially, it was more enterprise-centric, but now they have made it a customer platform official. NVIDIA (Vice president of Omniverse at NVIDIA) stated that; Frank DeLise “Omniverse provides powerful development tools for those who want to do ,” business growth in the gaming industry today He even added; “ to unify artists, tools and applications on a single platform can set an example for developer organizations around the world.” The ability of this system 1. What is Audio2Face? 2. How does Audio2Face Work? 3. How does Audio2Face ease the animation? 4. What are the system requirements for PC for Omniverse AI? 5. Why Does The World Need An Omniverse? 6. What Are The Benefits Of The Omniverse Audio2Face Application? 7. What are Metahumans? 8. What is NVIDIA Omniverse enterprise? 9. What Are The Available Connectors In 3D Ecosystems? 10. How does it help develop a Game with Unreal Engine 5? 11. How Can It Be Used In the Metaverse Application? What is Audio2Face? Audio2face is wholly based on artificial intelligence that contributes to lip synchronization and generates facial motion from an audio source. It offers a complete character pipeline transfer by using a straightforward workflow. A conventional character pipeline transfer provision allows the characters to drive with Audio2Face technologies. How Does Audio2Face Work? Audio2Face is the application of the Omniverse. Its job is to simplify the complex procedure of converting an audio input into an animated facial expression. The input file is in the shape of audio format .mp3 and .wav files. After getting processed, the output produced is completely realistic in nature. This is completely an AI-based technology that provides excellent methods of generating facial animation for the conventional content creation pipeline. How Does Audio2Face Ease The Animation? The Audio2Face eases the animation by simply matching the voice track. This is a hassle-free lip synchronization of animated gaming characters. It also gives background voice to the NPC characters and virtual assistants. The users utilize modern solutions to create interactive apps that export caches in a conventional pipeline. What Are The System Requirements For PC In Omniverse AI? Elements System Requirements Operating System Windows 10 Current Recommended Drivers 471.41 (Windows)/ 470.129.06 (Linux ) CPU Intel I7, AMD Ryzen, 2.5GHz or greater Microprocessor 64 bit Storage >= 500 Gb SSD RAM >=16 GB GPU Any RTX GPU VRAM >= 6 GB Why Does The World Need An Omniverse? Omniverse is developed to ease the workflow process without wasting any time. The platform is developed to make the applications more robust and scalable. People are now more interested in using apps that have created massive evolutions keeping in mind the upgraded technical aspects. It’s well understood that the metaverse creates a virtual world that represents the person in the digital space in the form of avatars. This is a kind of new experience, and a fascinating one but omniverse has surpassed this attribute. New quality elements are added to this magnificent platform, providing a unique experience to everyone associated with it. Similarly to the metaverse, it has surpassed multiverse by introducing an advanced feature set, and there is an easy importing of assets from existing tools. The omniverse is entirely new and on the verge of expanding. It has also introduced the concept of metahumans which is completely AI-enabled. The metahumans' role in the unreal gaming engine has proved to be amazing. This framework develops realistic human-like characters, i.e., photorealistic digital humans. What Are The Benefits Of The Omniverse Audio2Face Application? AI-driven: The omniverse Audio2Face exhibits more natural and sophisticated facial movements and expressions. AI has the power to capture every single moment very accurately. This entices the users to generate interactive AI avatars combined with computer vision and organic language understanding for AI assistant creation. These can be personalized as per every market industry. These virtual assistants facilitate help in making restaurant orders, banking transactions, appointments, or reservations in several sectors. They are liable to give tough competition to humans in the future. There is the simulation of various technologies that can quickly build real-time applications. Its benefit can also be seen in the Wildfires, which are one of the biggest concerns today. one of the best aerospace companies and the omniverse are working on the fight against the same. There is a great collaboration where AI simulation and the digital twin simulation will provide an in-advanced visualization technique to know the fire forecast to suppress it before it slips off. No Language Dependency Certain platforms are language specific. But omniverse Audio2Face comes with independent language selection. It is because it supports facial expressions. This is an advanced move with absolutely no chance of any mistake. Everything will be judged based on voice recognition and lip synchronization. The sensory voice is taken as the input, then processed, and the app receives the final outcome. What Are Metahumans? are the digital characters that are a part of unreal engine games.  There is the transmutation of the metahumans into a virtual world by creating characters that seem to be realistic. A variety of characters surely transports everything from the Epic Games Marketplace. In the unreal engine, there is the proper setting of the metahumans that captures the complete body including facial expressions. MetaHumans Key Features Convenient to use These digital humans are easy to handle. No instruction manual is needed. Go through the database, choose the initial point from different ranges, and then redefine the characters with the help of the sculpting tools. All this is done just by dragging the assets. Extensive variability The character's features, like eyes, hands, teeth, nose, etc., are a huge possibility for picking up the faces for the projects. The user selects the body type, and the apparel is picked from different toolsets to have a perfect metahuman. MetaHuman creators have the power to develop everything from real-world scenarios. So the adjustments and any changes are made based on the data collected. The data is extremely limited. Therefore a metahuman is very easily created. Then, it comes to the other things like choosing the skin tones and hair color to make the characters close to the actual one. 3D Rigging The metahumans, along with complete body parts, give authentication to create animation in the Unreal engine. The 3d animation rigging is done with the help of Speech graphics, digital domain, motion solutions, and ARKit. Included Source Data The metahumans created have the source data included. It is usually in the form of Maya File, an AI-enabled auto desk. This encompasses the meshes, animation controls, facial rigs, Skelton, and more. The metahuman plugin that applies to the unreal engine allows the conversion of the personalized mesh into a completely rigged metahuman. More alternatives correspond to clothing and hair to give a pragmatic look. https://www.youtube.com/watch?v=h3xT_F_tEuU?embedable=true What is NVIDIA Omniverse Enterprise? NVIDIA omniverse enterprise will help various teams develop 3D internet and metaverse apps. There is a lot of complexity faced with a wide collection of software toolkits that provide designing which is true to reality. The following characteristics are part of an omniverse enterprise; https://www.youtube.com/watch?v=IxQZEhDlt5g?embedable=true Personalized and Extensible existing ones through Python script. Omniverse Enterprise allows designers and developers to build highly advanced 3D pipelines with AI-enabled functionalities to develop connections with Completely Enterprise-based The downtime of the system is reduced and allows critical projects to stay on track with NVIDIA’s enterprise-level support. Scalability Omniverse is known for manually accelerating the simulation and the visualization directly from the desktop to the data center. What Are The Available Connectors In 3D Ecosystems? Autodesk 3Ds Max Epic Games Unreal Engine Reallusion Actor Core Autodesk Maya Ipolog Graphisoft Archicad HDR light studio Synctwin SketchFab Trimble SketchUp How Does It Help Develop A Game With Unreal Engine 5? Gamers' expectations are increasing drastically, and they want to explore more to satisfy gamers' demands. The players are fans of the immersive world with huge libraries consisting of 3D content. This is a very daunting task, and much money is invested in structuring the game. The integration of AI combined with fantastic lighting elements. There are a  lot of challenges that are faced by the creators, developers, and users that need to be resolved. The biggest advantage is to allow the artists and designers to perform the work together, and that too in real-time. Big firms like NVIDIA and Adobe have worked together uniquely to give a 3D experience to users. Such is the craze of people towards this excellent platform that more than 50k creators have already downloaded. Omniverse can quickly take on challenges and support developers in developing a photorealistic game. This contributes to building seamless relations in-between artists, software tools, and assets on a single platform. Many established film studios in Hollywood have adopted USD. There is the integration of real-time tracing graphics. This can be seen in the unreal engine that completely supports USD. The USD offers versatile features. Gaming is undoubtedly a dynamic and vibrant realm that should provide a fantastic gaming experience. How Omniverse Be Used In the Metaverse Application? Omniverse has the potential to allow persons and larger organizations to have digital collaboration across various industry sectors. Some workflows are equally complicated and stimulating because of the 3D workflow. It has limitations due to larger user connectivity, tools involved in content creation, focus on artificial intelligence, etc. Integrating the omniverse in the metaverse includes AI models for the smooth creation of digital avatars. This makes the user's experience in terms of performing any activity interactive. There is a great advancement in the USD (universal scene description), an open-source format that depicts the three-dimensional representation of the graphics and the platforms involved. The basic functioning of the omniverse in the metaverse is to give special and next-level visual effects to give a fully immersive experience to the people. It has its application in multiple domains like engineering and architecture, giving complete support to the "Industrial Metaverse," a new thing in the making. Various real-world industries want to explore the metaverse to serve various sectors like healthcare, finance, retail, manufacturing, etc. It is also helpful in developing metaverse applications and creating more effective automobile infrastructure related to vehicles and factories. Bottom Line!! As the metaverse is enjoying immense popularity in the virtual world, the next big thing that has started creating ripples in the 3D world is the omniverse. Its implementation is viable in every sector and creates digital twins for real-world simulation. Omniverse provides effective protocols and standards that allow services for the apps and the customers. It's an amalgamation of programming languages and concepts related to physics. The users can teleport themselves into the omniverse with VR and return to the real world with AR. On the whole, it can be said that the omniverse has the potential to transform every industry. This can be used in the workplace where the employees can be connected with each other in real-time by 3D tools. This helps in achieving swift production with the help of full-service enterprise support.

Omniverse Audio2Face: An End-to-End Platform For Customized 3D Pipelines

It’s going to be really funny watching marketers and agencies completely f*ck up their #metaverse campaigns because they think people will behave the same as they do offline. If you look at the marketing and advertising industry, it’s been split into online and offline marketing already — but this is NOT the same thing. Offline and Online Marketing Offline marketing is any marketing that is not done through the internet. This encompasses various mediums and formats, from TV and radio commercials to billboards and print ads. Online marketing communicates to customers through e-mails, chat, and social media, for example. People are tracked through their interactions with QR codes, offers and discount vouchers, and even loyalty schemes. The Metaverse But the metaverse is far beyond that simple definition. The metaverse represents another world for people to interact with but is as immersive as the physical one, and as such, will present people with the opportunity to behave in ways completely contrary to themselves. As a result, it’s going to present an entirely different set of behavioral data you’ll need to think about very quickly. And with the attention firmly on the ability of users to retain some data sovereignty and control online, that data might start becoming harder to get hold of. If you think you’ve cracked omnichannel marketing in this brave new world, you’re sadly mistaken. Who a person is and how they behave in person will be an entirely different proposition to how they behave “in-avatar” interacting in a virtualized 3D environment. Marketers will make a huge mistake in thinking that they understand their customer’s psyche because they’ve already collected enough data on their habits in the real world. We’re aware of it ourselves when we play open-world video games that allow us the freedom to be who we want to be — many of us want that escape, and so expecting customers not to take advantage of the opportunity to express themselves differently will cost marketers dearly. Why would you send me offers based on my propensities as I shop and spend physically to my online persona where I may want to separate those lives and spend and behave in a manner that suits the worlds I want to inhabit? Take Disney, for example — they know that when you enter a theme park you’re going to behave differently than if you were just out shopping for clothes or dinner because it’s an escape. A metaverse or #web3 experience will be no different. And don’t get me started on having to think about loyalty schemes in completely different dimensions too, because those too just won’t translate the same way either. We’re now going to have to think of marketing beyond just omnichannel and think ‘omniverse’, and I’m not sure you’re going to be prepared for just how customers will behave so differently in either. Also published here

R.I.P. Omnichannel Marketing — Long Live Omniverse Marketing

2021 has been a pretty amazing year for Omnisearch. Having started initial work on Omnisearch at the end of 2020, we entered the new year with a working MVP yet no revenue, no significant partnerships, and no funding. Fast forward to the end of 2021, and we now have fantastic revenue growth, a partnership with a public company, and a far more powerful, complete and polished product. But one milestone really changed Omnisearch’s trajectory: our $450,000 USD pre-seed round by GoAhead Ventures. In this post I want to share the story of how it came about and offer a couple of takeaways to keep in mind when preparing for fundraising. The story Contrary to most advice, my co-founder Matej and I didn’t allocate a specific time to switch to “fundraising mode” but rather talked to investors on an ongoing basis. It was a bit of a distraction from working on the product, but on the positive side we were able to constantly get feedback on the idea, pitch, go-to-market strategy and hiring, as well as hearing investors’ major concerns sooner rather than later. That being said, our six-month long fundraising efforts weren’t yielding results - we talked to about twenty investors, mostly angels or smaller funds, with no success. The feedback was generally of the “too early for us” variety (since we were still pre-revenue), with additional questions about our go-to-market strategy and ideal customer persona. The introduction to our eventual investors, California-based GoAhead Ventures, came through a friend who had pitched them previously. We wrote a simple blurb and sent our pitch deck. We then went through GoAhead’s hyper-efficient , consisting of a 30-minute call, a recorded three-minute pitch, and filling out a simple Google doc. screening process Throughout the whole process, the GoAhead team left an awesome impression thanks to their knowledge of enterprise software and their responsiveness. They ended up investing and the whole deal was closed within two weeks, which is super fast even by Silicon Valley standards. While our fundraising experience is a single data point and your case might be different, here are the key takeaways from our journey. Like I said above, we talked to about twenty investors before we closed our round. Getting a series of “no”s sucks, but we took the feedback seriously and tried to prepare better for questions that caught us off guard. But we persevered, keeping in mind that from a bird’s eye perspective it’s an amazing time to be building startups and raising funds. Perseverance wins: Sounds pretty obvious, right? The truth is, though, that even a small amount of revenue is infinitely better than none at all. One of the major differences between our eventual successful investor pitch and the earlier ones was that we had actual paying customers, though our MRR was low. This allows you to talk about customers in the present tense, showing there’s actual demand for your product and making the use cases more tangible. And ideally, highlight a couple of customer testimonials to boost your credibility. Focus on traction: In Omnisearch’s case, the demo was oftentimes the best received part of the pitch or call. We’d show investors the live demo, and for bonus points even asked them to choose a video from YouTube and then try searching through it. This always had a “wow” effect on prospective investors and made the subsequent conversation more exciting and positive. Have a demo ready: Accelerators like Y Combinator or Techstars can add enormous value to a startup, especially in the early stages. And while it’s a great idea to apply, don’t rely on them too heavily. Applications happen only a few times a year, and you should have a foolproof fundraising plan in case you don’t get in. In our case, we just constantly looked for investors who were interested in our space (defined as enterprise SaaS more broadly), using LinkedIn, , and intros from our own network. Accelerators: AngelList Pitching is tough to get right even for seasoned pros, so it pays to practice as often as possible. We took every opportunity to perfect the pitch: attending meetups and giving the thirty-second elevator pitch to other attendees over beer and pizza, participating in startup competitions, going to and exhibiting at our own booth, attending , and pitching to friends who are in the startup world. As our friends at Wasp say, the pitch gets better ! Practice the pitch ad nauseam: conferences pre-accelerator programs after the 50-th time Frankly, this was one of the strongest parts of our pitch and investor conversations. If you’re in a similar space to ours, and are an awesome resource, as well as sites like or and . By thoroughly studying these resources we gained a great understanding of the industry landscape and were able to articulate our differentiation more clearly and succinctly. Presenting this visually in a coordinate system or a is, from our experience, even more effective. Show an understanding of the competition: Gartner Magic Quadrants Forrester Waves AlternativeTo Capterra G2 feature grid Getting your first round of funding is just the beginning of the journey, so it’s important to avoid euphoria and get back to building and selling the product as soon as possible. While securing funding enables you to scale the team, and is a particular relief if the founders had worked without a salary, the end goal is still to build a big, profitable, and overall awesome startup. Remember it’s just the beginning!

We Raised $450k For Omnisearch’s  Pre-Seed Round: Here's What We Learned

Omniversity is the first university in VR, born from the insatiable desire to access forward-looking education in a playful, immersive, sensorial, holistic manner. The initiative combines education and entertainment into edutainment. Designed as a multiplayer VR game app, students of all ages and nationalities can enroll in experiential learning programs across multiple disciplines. The VR application offers learning experiences at scale, enabling every student to have access to music labs, science labs, film labs or to worldwide travelling, and experience things that they wouldn’t have access to otherwise. The modern classroom can have a place for everyone - children and adults from a multitude of cultural and social backgrounds from all around the world. The VR application aims to revolutionize learning beyond basic memorization and observation. The students can interact with the material at their own pace. Each class is designed as an interactive simulation where the player learns by doing. Students are invited to learn about a subject by living it. The Post-Pandemic Context The pandemic has greased the wheels for digital world’s entrée into higher education, disrupting an education system that many assert was already losing its relevance. Schools and universities continue to focus on traditional textual-based academic skills and rote learning, rather than on high level skills such as adaptability, critical thinking, empathy or environmental awareness, which will be more important for achieving success in the future. A lot of current learners have difficulties in comprehending information. Students today declare to feel bored, tired, disengaged, and usually not sure why they are learning about a topic in the first place. Universities are still maintaining high prices for what has become, overnight, a dramatically less compelling product offering. The value of formal education has been substantially degraded. In this context, Omniversity was created as a joint venture of artists, educators and technologists, to offer the long-awaited possibility for learners to study what/when/with whom they wish, achieving a balance between highly personalized learning and collaborative practices. Transdisciplinary Dream Team Freshly created in 2020, Omniversity is a start-up with a global scalable potential. The project is founded by Ioana Mischie (CEO), transmedia artist/futurist, joined by Andra Bria (CPO), product designer and innovator. Both co-founders have connected in San Francisco during a global hackathon where they have received awards for their proposals. With extensive experience in entertainment and creative media, both believe that education needs a boost of playfulness and innovation.  As a multi-awarded writer/director, Ioana Mischie also activated as a Fulbright Scholar at USC -School of Cinematic Arts in Los Angeles, California and is  teaching transmedia arts at UNATC and UBB and leading classes in collaboration with international universities.  Andra Bria founded Craft Product School, a product innovation school, created an Augmented Reality book and leads numerous international workshops on product design. “ .”  believes Ioana Mischie, who initiated the backbone idea of Omniversity. Omniversity is, above all, our manifesto for holistic education. By bringing together the most innovative minds in education, arts, technology, science, storytelling and game design, we are hoping to form not only a pioneering community of experiential-minded students, but rather to facilitate a community of visionaries that might dare to advance humankind “ . “ adds Andra Bria, who leads the product design strategy. The way we educate and retrain ourselves will be increasingly important in a world where knowledge is taken over by machines. How can we make sure that education is augmenting our capabilities, helping us achieve our full potential, and does that in an inclusive, personalized, and last but not least, in a fun and engaging way? These are the questions that we’re trying to answer through Omniversity, and we couldn’t be more excited about this challenge The team is joined by Emil Drăgan as technological strategist and CTO of the start-up, who is a passionate creator of AR and VR and who has signed, so far, numerous immersive projects aiming to upgrade contemporary education. The project relies on an extensive transdisciplinary team. Current status & Call to Action In the current phase of development, the pioneering initiative relies on the input of professors and students worldwide. The team has launched a addressed to all educators and practitioners that aim to translate their practice into enriching immersive experiences. Call for Entries Students that are interested to test the preliminary versions of the tool are invited to register . here The Future of Education: global connectivity, immersion & socialization As numerous institutions and theorists aimed to analyze education in pandemic times, multiple scenarios have emerged. While schools are struggling to integrate hybrid methodologies, there is still a lack of alternatives when it comes to innovative learning. Omniversity gamifies the learning experience while also encouraging real life learning experiences and opportunities and outdoor exploration. The approach is highly modular, allowing virtual classes to be mostly seen as teasers for societal exploration. “ We aim to facilitate a multi-layered understanding of the world and to empower elevated forms of global teamwork. Although VR as a medium is often regarded as a form of profound isolation, we aim to repurpose it as a solidarity engine.” highlighted Ioana Mischie. Omniversity will allow students who are physically distant to experience learning as if they are in the same classroom. The VR application promotes group learning and socialization at an international level: students from diverse regions can come together to discuss, synthesize and learn from one another. At the moment, the initiative is in an advanced development stage, the company actively looking for funding, as well as partnership opportunities with transdisciplinary professionals and learners from around the world. Also published at: https://business-review.eu/remoteeducation/the-future-of-education-is-here-omniversity-the-first-university-in-vr-213979

The Future of Education is OMNIVERSITY: The First VR University

Table of Links Abstract and I. Introduction II. Background III. Related Works IV. Comparative Analysis V. Findings and Evaluation VI. Conclusion and References VI. CONCLUSION This research paper explored multiple scalability solutions both at layer-one and layer-two of the stack. The topics explored looked at how different protocols intend to increase the transaction throughput for a blockchain network and a comparative examination of these solutions was performed. The analysis was contextualised their feasibility in different use cases, and system implementations. Each scalability aspect explored was analysed regarding how they balanced security and decentralisation from the blockchain trilemma, while improving the horizontal scaling of systems. We observed that there was a lack of scalability integrations across different layers that was attributed to horizontally scaling on both the layer-one with state sharding and and layer-two with smart contract implementations such as rollups or nested-chains. Most research identified was surrounding account model scalability, and very minimal work was done to explore smart contracts can be ran in parallel with UTXO. We outlined a solution using making use of an extended and more expressive UTXO model, supporting paralellized UTXO state sharding, and support for smart contract dependant scaling through Zero-Knowledge rollups and nested side-chains. A. Future Works We intend to implement a layer one test-chain using a eUTXO state transaction model, adapting OmniLedger inspired protocols to it. Smart contract integration and testing will occur once this is complete, and the viability of various approaches and architectures will assessed. REFERENCES [1] J. Dattani and H. 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Johnson, P. Robinson, and J. Brainard, “Sidechains and interoperability,” arXiv , Mar 2019. [Online]. Available: https://arxiv.org/abs/1903.04077v2 [35] D. T. L. D. C. E. L. Q. Dang, H. and B. Ooi, “Towards scaling blockchain systems via sharding.” In Proceedings of the 2019 international conference on management of data, pp. 123 – 140., 2019. [36] H. A. Hafid, A. and M. Samih, “New mathematical model to analyze security of sharding-based blockchain protocols,” IEEE Access. , pp. 185 447–185 457., 2019. [37] F. Sanchez, “Cardano’s Extended UTXO accounting model – built to support multi-assets and smart contracts (part 2) - IOHK Blog,” Mar 2021, [Online; accessed 21. Jan. 2022]. [Online]. Available: https://iohk.io/en/blog/posts/2021/03/12/ cardanos-extended-utxo-accounting-model-part-2 Authors:
(1) Cayo Fletcher-Smith, Bournemouth University;
(2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background II. Background III. Related Works III. Related Works IV. Comparative Analysis IV. Comparative Analysis V. Findings and Evaluation V. Findings and Evaluation VI. Conclusion and References VI. Conclusion and References VI. CONCLUSION This research paper explored multiple scalability solutions both at layer-one and layer-two of the stack. The topics explored looked at how different protocols intend to increase the transaction throughput for a blockchain network and a comparative examination of these solutions was performed. The analysis was contextualised their feasibility in different use cases, and system implementations. Each scalability aspect explored was analysed regarding how they balanced security and decentralisation from the blockchain trilemma, while improving the horizontal scaling of systems. We observed that there was a lack of scalability integrations across different layers that was attributed to horizontally scaling on both the layer-one with state sharding and and layer-two with smart contract implementations such as rollups or nested-chains. Most research identified was surrounding account model scalability, and very minimal work was done to explore smart contracts can be ran in parallel with UTXO. We outlined a solution using making use of an extended and more expressive UTXO model, supporting paralellized UTXO state sharding, and support for smart contract dependant scaling through Zero-Knowledge rollups and nested side-chains. A. Future Works A. Future Works We intend to implement a layer one test-chain using a eUTXO state transaction model, adapting OmniLedger inspired protocols to it. Smart contract integration and testing will occur once this is complete, and the viability of various approaches and architectures will assessed. REFERENCES [1] J. Dattani and H. Sheth, “Overview of blockchain technology.” Asian Journal of Convergence in Technology, pp. 123 – 140., 2019. [2] Q. Zhou, H. Huang, Z. Zheng, and J. Bian, “Solutions to Scalability of Blockchain: A Survey,” IEEE Access, vol. 8, pp. 16 440–16 455,Jan 2020. [3] W. T. Jiacun Wang, Formal Methods in Computer Science. Andover, England, UK: Taylor & Francis, Jul 2019. [4] L. N. Nguyen, T. D. T. Nguyen, T. N. Dinh, and M. T. Thai, “OptChain: Optimal Transactions Placement for Scalable Blockchain Sharding,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, Jul 2019, pp. 525–535. [5] L. Brunjes and M. Gabbay, “UTxO- vs account-based smart contract blockchain programming paradigms,” Mar 2020. [Online]. Available: https://www.semanticscholar.org/ paper/UTxO-vs-account-based-smart-contract-blockchain-Br%C3% BCnjes-Gabbay/ea415af5a99f27866d4a74601187d10a538fea50 [6] M. M. Chakravarty and J. Chapman, “The Extended UTXO Model,” Feb 2020, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://iohk.io/en/research/library/papers/the-extended-utxo-model [7] M. Bellare, S. Halevi, A. Sahai, and S. Vadhan, “Many-to-one trapdoor functions and their relation to public-key cryptosystems,” in Advances in Cryptology - CRYPTO ’98. Berlin, Germany: Springer, May 2006, pp. 283–298. [8] A. M. Antonopoulos and G. Wood, Mastering Ethereum. Sebastopol, CA, USA: O’Reilly Media, Inc., Nov 2018. [Online]. Available: https://www.oreilly.com/library/view/mastering-ethereum/ 9781491971932/ch04.html [9] V. Buterin, “Ethereum whitepaper,” ethereum.org, 2021. [Online]. Available: https://ethereum.org/en/whitepaper [10] F. Santini, S. Bistarelli, I. Mercanti, and P. Santancini, “End-to-End Voting with Non-Permissioned and Permissioned Ledgers,” Journal of Grid Computing, vol. 17, no. 3, Mar 2019. [11] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://www.ussc.gov/sites/default/files/ pdf/training/annual-national-training-seminar/2018/Emerging Tech Bitcoin Crypto.pdf [12] J. Lee, “Dappguard : Active monitoring and defense for solidity smart contracts,” 2017. [Online]. Available: http://courses.csail.mit.edu/6.857/ 2017/project/23.pdf [13] C. F. Tavares, B. and A. Restivo, “A survey on blockchain technologies and research.” J. Inf,, pp. 118–128, 2019. [14] R. D. L. S. Buterin, V. and G. Piliouras, “Incentives in Ethereum’s hybrid Casper protocol.” International Journal of Network Management, 30(5), p. e2098, 2020. [15] W. L. O. S. A. M. Tseng, L. and J. Othman, “Blockchain for managing heterogeneous internet of things: A perspective architecture.” IEEE network, 34(1),, pp. 16–23., 2020. [16] S. M. N. C. M. K. Dhumwad, S. and S. Prabhu, “A peer to peer money transfer using SHA256 and Merkle tree.” In 2017 23RD Annual International Conference in Advanced Computing and Communications (ADCOM), pp. 40 – 43., 2017. [17] J. Frankenfield, “What is a merkle root ?” Aug 2021, [Online; accessed 29. Nov. 2021]. [Online]. Available: https://www.investopedia.com/ terms/m/merkle-root-cryptocurrency.asp [18] N. V. Z. C. B. K. G. S. Luu, L. and P. Saxena, “A secure sharding protocol for open blockchains,” 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 17–30, 2016. [19] V. J. P. D. Goldberg, S. and L. Reyzin, “Verifiable random functions (VRFs)..” ., p. ., 2018. [20] E. Kokoris-Kogias, P. Jovanovic, L. Gasser, N. Gailly, and E. Syta, “OmniLedger: A Secure, Scale-Out, Decentralised Ledger via Sharding,” in 2018 IEEE Symposium on Security and Privacy (SP). IEEE, May 2018, pp. 583–598. [21] Z. S. X. G. G. Y. L. Y. X. J. Xi, J. and X. Zhang, “A Comprehensive Survey on Sharding in Blockchains. Mobile Information Systems,” Mobile Information Systems, 2021., 2021. [22] Z. Dong, L., “The Zilliqa Project: A Secure, Scalable Blockchain Platform.” ., p. ., 2018. [23] K. Y. Kim, S. and S. Cho, “A survey of scalability solutions on blockchain.” In 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1204–1207., 2018. [24] C. K. P. R. Z. Q. D. A. Singh, A. and K. Choo, “Sidechain technologies in blockchain networks: An examination and state-of-the-art review.” Journal of Network and Computer Applications,, p. 102471., 2020. [25] J. Poon and V. Buterin, “Plasma: Scalable autonomous smart contracts,” plasma.io, 2017. [Online]. Available: http://plasma.io/plasma.pdf [26] C. Sguanci, R. Spatafora, and A. M. Vergani, “Layer 2 Blockchain Scaling: a Survey,” arXiv, Jul 2021. [Online]. Available: https: //arxiv.org/abs/2107.10881v1 [27] “An Incomplete Guide to Rollups,” Jan 2021, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://vitalik.ca/general/2021/01/05/ rollup.html [28] T. Schaffner, “Scaling public blockchains — a comprehensive analysis of optimistic and zero-knowledge rollups,” 2021. [Online]. Available: https://wwz.unibas.ch/fileadmin/user upload/wwz/00 Professuren/ Schaer DLTFintech/Lehre/Tobias Schaffner Masterthesis.pdf [29] F. Bruschi, M. Tumiati, V. Rana, M. Bianchi, and D. Sciuto, “A scalable decentralized system for fair token distribution and seamless users onboarding,” Blockchain: Research and Applications , p. 100031, Oct 2021. [30] “Optimism Docs | Optimism Docs,” Jan 2022, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://community.optimism.io [31] “Arbitrum Developer Quickstart · Offchain Labs Dev Center,” Jan 2022, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://developer.offchainlabs.com/docs/developer quickstart [32] “Introduction to zkSync for Developers | zkSync: secure, scalable crypto payments,” Jan 2022, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://zksync.io/dev [33] “StarkEx Introduction,” Jan 2022, [Online; accessed 20. Jan. 2022]. [Online]. Available: https://docs.starkware.co/starkex-v4 [34] S. Johnson, P. Robinson, and J. Brainard, “Sidechains and interoperability,” arXiv , Mar 2019. [Online]. Available: https://arxiv.org/abs/1903.04077v2 [35] D. T. L. D. C. E. L. Q. Dang, H. and B. Ooi, “Towards scaling blockchain systems via sharding.” In Proceedings of the 2019 international conference on management of data, pp. 123 – 140., 2019. [36] H. A. Hafid, A. and M. Samih, “New mathematical model to analyze security of sharding-based blockchain protocols,” IEEE Access. , pp. 185 447–185 457., 2019. [37] F. Sanchez, “Cardano’s Extended UTXO accounting model – built to support multi-assets and smart contracts (part 2) - IOHK Blog,” Mar 2021, [Online; accessed 21. Jan. 2022]. [Online]. Available: https://iohk.io/en/blog/posts/2021/03/12/ cardanos-extended-utxo-accounting-model-part-2 Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. Authors: Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

A Potential Solution to Blockchain Bottlenecks

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

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

Table of Links Abstract and I. Introduction II. Background III. Related Works IV. Comparative Analysis V. Findings and Evaluation VI. Conclusion and References IV. COMPARATIVE ANALYSIS This section examines the impact on each property of the blockchain trilemma when implementing layer-one and layer-two scaling solutions across UTXO and account-based transaction models. Comparable metrics are up-scale factors, transaction throughput and dispute resolution. Qualitative comparison will be used to express the strengths and limitations of decentralisation and security. A. Layer-One Sharding The works of Luu et al. in Elastico presented a decentralised scalable solution to sharding and parallelisation, proposing feasible approaches to shard assignment without central coordination [18]. Although scaling increases with network growth linearly, individual shard security is sacrificed. This culminates in an 8% average shard failure probability, having negative security implications and undermining scaling through failure bottlenecks [21]. Resilience to byzantine threats decreases consistently, as shards are added, meaning infinite scaling eventually completely sacrifices system integrity. This can be partly attributed to small shard sizes [20]. Kokris-Kogias et al address bottleneck and security issues native to Elastico scaling through their work on OmniLedger. By increasing shard size byzantine fault tolerance is more effectively managed in a decentralised setting. The implementation of an atomic commit protocol directly combats double spending, an issue imperfectly solved in industry approaches previous to OmniLedger. Cross-shard atomicity ensures UTXO funds aren’t indefinitely locked as pending in-cases of malicious coordination. This was achieved through rejection and acceptance proof, with resilient commit and rollback parameters [20]. Probabilistically, OmniLedger could survive for 230 years before complete shard failure undermines the blockchain. In contract Elastico can survive 1 hour, under the same parameters [35]. Zilliqa utilises directory service shards to increase security and transaction efficiency through transaction coordination This mitigates double-spending and is a scalable solution to transaction sharding. Transaction speeds don’t scale as highly compared to RapidChain or OmniLedger, however transaction assignment is more secure[36]. B. Layer-Two Scalability Plasma follows a nested, parent/child chain architecture built on Ethereum [9]. Although Ethereum supports a high level of decentralisation, Plasma can centralise aspects of processing activities due to centric coordination on some sidechains [25]. Security challenges are found in consensus, weakening the overall byzantine fault tolerance of the network. For these reasons Plasma severely sacrifices security and decentralisation in the pursuit of scalability. Compared to Plasma ZKsync achieves far greater decentralisation and security at scale. Despite significantly lower transaction speeds and latency, ZKsync manages all three attributes of the blockchain trilemma while achieving impactful scalability. The zero-knowledge nature of this solution is privacy preserving by default, increasing overall system security. ZKsync is fully dependent on smart contract implementation to aggregate transaction processing, making it infeasible for UTXO based projects [32]. Nested-chains like Plasma can be deployed over a wider array of layer-one implementations, making them more feasible for less specialised use-cases. Contrasting ZKsync and zero-knowledge rollups in general, optimistic roll ups like Arbitrum focus on hyper-scalability, with disregard for other decentralisation. Supporting the highest scaling of any solution at both layer-one and two, Arbitrum is a prime example of the trilemma. The use of trusted centralised sequencers, undermines the decentralised aims of all permissionless blockchain projects. Security is increased through fraud proofs, although slow generation increases transaction latency. Although transactions are processed quickly, latency causes large time windows until a transaction is immutably confirmed. C. State Transaction Models The core UTXO account model used by Bitcoin was designed to handle transactions from one user to anothers private key address. The way the unspent transactions are generated and spent means that these transactions have a greater chance of being scaled by processing them in parallel. Although the UTXO model has minimal expressiveness in terms of on-chain logic, the UTXO model enables scalability and confidentiality, with much simpler transaction verification [11]. Vitalik introduced the Account-model into the Etheruem blockchain, which runs on a global virtual machine which maintains a record of all transactions, users and even smart contracts. Accounts are ran by private keys or smart contracts and store user assets as account balance. Although storing account state within the global state makes things easier, it also limits scalability options at layer one and requires the introduction of layer 2 solutions such as Plasma [9]. More recently, Cardano’s IOHK developed the eUTXO model, which maintains the core aspect of the UTXO model with regards to how each transaction consumes unspent outputs in order to produce new unspent transactions that can be further spent. As Cardano intended to implement smart contract functionality, using the basic UTXO model that was designed to do little more than handle payments would be unsuitable. This is highlighted through the lack of expressive coded logic available on-chain to UTXO systems. The use of an Account model addresses the issue of limited expressiveness by developing specialised smart contract accounts, becoming far to complex [37]. To create an eUTXO, two core additional pieces of functionality are needed to build upon the base UTXO model. The model needed to ensure that smart contract state was maintained and also enforce that the same smart contract logic is used throughout the whole sequence of transactions. This eUTXO model enables native-assets and smart contracts to be used on the UTXO model for the first time [6]. Authors:
(1) Cayo Fletcher-Smith, Bournemouth University;
(2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background II. Background III. Related Works III. Related Works IV. Comparative Analysis IV. Comparative Analysis V. Findings and Evaluation V. Findings and Evaluation VI. Conclusion and References VI. Conclusion and References IV. COMPARATIVE ANALYSIS This section examines the impact on each property of the blockchain trilemma when implementing layer-one and layer-two scaling solutions across UTXO and account-based transaction models. Comparable metrics are up-scale factors, transaction throughput and dispute resolution. Qualitative comparison will be used to express the strengths and limitations of decentralisation and security. A. Layer-One Sharding A. Layer-One Sharding The works of Luu et al. in Elastico presented a decentralised scalable solution to sharding and parallelisation, proposing feasible approaches to shard assignment without central coordination [18]. Although scaling increases with network growth linearly, individual shard security is sacrificed. This culminates in an 8% average shard failure probability, having negative security implications and undermining scaling through failure bottlenecks [21]. Resilience to byzantine threats decreases consistently, as shards are added, meaning infinite scaling eventually completely sacrifices system integrity. This can be partly attributed to small shard sizes [20]. Kokris-Kogias et al address bottleneck and security issues native to Elastico scaling through their work on OmniLedger. By increasing shard size byzantine fault tolerance is more effectively managed in a decentralised setting. The implementation of an atomic commit protocol directly combats double spending, an issue imperfectly solved in industry approaches previous to OmniLedger. Cross-shard atomicity ensures UTXO funds aren’t indefinitely locked as pending in-cases of malicious coordination. This was achieved through rejection and acceptance proof, with resilient commit and rollback parameters [20]. Probabilistically, OmniLedger could survive for 230 years before complete shard failure undermines the blockchain. In contract Elastico can survive 1 hour, under the same parameters [35]. Zilliqa utilises directory service shards to increase security and transaction efficiency through transaction coordination This mitigates double-spending and is a scalable solution to transaction sharding. Transaction speeds don’t scale as highly compared to RapidChain or OmniLedger, however transaction assignment is more secure[36]. B. Layer-Two Scalability B. Layer-Two Scalability Plasma follows a nested, parent/child chain architecture built on Ethereum [9]. Although Ethereum supports a high level of decentralisation, Plasma can centralise aspects of processing activities due to centric coordination on some sidechains [25]. Security challenges are found in consensus, weakening the overall byzantine fault tolerance of the network. For these reasons Plasma severely sacrifices security and decentralisation in the pursuit of scalability. Compared to Plasma ZKsync achieves far greater decentralisation and security at scale. Despite significantly lower transaction speeds and latency, ZKsync manages all three attributes of the blockchain trilemma while achieving impactful scalability. The zero-knowledge nature of this solution is privacy preserving by default, increasing overall system security. ZKsync is fully dependent on smart contract implementation to aggregate transaction processing, making it infeasible for UTXO based projects [32]. Nested-chains like Plasma can be deployed over a wider array of layer-one implementations, making them more feasible for less specialised use-cases. Contrasting ZKsync and zero-knowledge rollups in general, optimistic roll ups like Arbitrum focus on hyper-scalability, with disregard for other decentralisation. Supporting the highest scaling of any solution at both layer-one and two, Arbitrum is a prime example of the trilemma. The use of trusted centralised sequencers, undermines the decentralised aims of all permissionless blockchain projects. Security is increased through fraud proofs, although slow generation increases transaction latency. Although transactions are processed quickly, latency causes large time windows until a transaction is immutably confirmed. C. State Transaction Models C. State Transaction Models The core UTXO account model used by Bitcoin was designed to handle transactions from one user to anothers private key address. The way the unspent transactions are generated and spent means that these transactions have a greater chance of being scaled by processing them in parallel. Although the UTXO model has minimal expressiveness in terms of on-chain logic, the UTXO model enables scalability and confidentiality, with much simpler transaction verification [11]. Vitalik introduced the Account-model into the Etheruem blockchain, which runs on a global virtual machine which maintains a record of all transactions, users and even smart contracts. Accounts are ran by private keys or smart contracts and store user assets as account balance. Although storing account state within the global state makes things easier, it also limits scalability options at layer one and requires the introduction of layer 2 solutions such as Plasma [9]. More recently, Cardano’s IOHK developed the eUTXO model, which maintains the core aspect of the UTXO model with regards to how each transaction consumes unspent outputs in order to produce new unspent transactions that can be further spent. As Cardano intended to implement smart contract functionality, using the basic UTXO model that was designed to do little more than handle payments would be unsuitable. This is highlighted through the lack of expressive coded logic available on-chain to UTXO systems. The use of an Account model addresses the issue of limited expressiveness by developing specialised smart contract accounts, becoming far to complex [37]. To create an eUTXO, two core additional pieces of functionality are needed to build upon the base UTXO model. The model needed to ensure that smart contract state was maintained and also enforce that the same smart contract logic is used throughout the whole sequence of transactions. This eUTXO model enables native-assets and smart contracts to be used on the UTXO model for the first time [6]. Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. Authors: Authors: (1) Cayo Fletcher-Smith, Bournemouth University; (2) Frazer Chard. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Layer-1 vs. Layer-2: Scaling Solutions and Their Effects on Blockchain Security and Decentralization

How the Blockchain Handles More Transactions as It Grows

Speaking at CES on 6 January 2025, Jensen Huang, CEO of NVIDIA, delivered the keynote address from the digitally recreated NVIDIA headquarters, painting a fascinating picture of how artificial intelligence is making impact across industries and everyday life. AI in Your Pocket: Introducing NVIDIA Project DIGITS One of the most intriguing announcements was NVIDIA's Project DIGITS, a new AI-powered personal supercomputer. Huang was impressed by its compact design, similar to the 'size of a pack of cigarettes', but capable of delivering a petaflop of computing power. This innovation aims to democratize AI supercomputing by making it accessible to small labs, lone professionals, and even home users (NVIDIA Project Digits). Consider a scenario where researchers have access to significant computational resources directly from their workstations. This eliminates dependencies on cloud computing or traditional supercomputers, which can be limited by availability and cost. In this setting, a scientist working in a smaller lab could conduct their research with greater autonomy, utilizing a powerful, localized supercomputer. This accessibility to high-performance computing would enable individual scientists and smaller teams within startups to engage in AI research more readily. This shift could lead to increased research and development activities, as barriers to computational resources are reduced. Data analysis and model training could be performed more quickly and iteratively. Consequently, this future could see more individuals and organizations participating in scientific and technological AI advancements. From Drug Discovery to Climate Solutions: The Power of AI Tokens "This is how intelligence is created,” Huang said, introducing the new era of AI development. He explained how AI tokens, the building blocks of intelligent systems, open up endless possibilities. Imagine a future in which AI-assisted drug discovery accelerates the development of life-saving medicines, climate change is mitigated through energy optimization and renewable energy solutions, and personalized education is made available to all as AI tutors adapt to individual learning styles. The RTX 50 Series Unveiled Moving on to hardware, Huang introduced the RTX 50 series, a new powerful GPU designed to accelerate AI workloads. With 92 billion transistors and offering 4,000 TOPS (terabytes of operations per second) for AI, it is three times more powerful than its predecessor, the Ada generation. Huang emphasized its usefulness in both gaming and professional applications: “This RTX 50 Series, Blackwell architecture... the GPU is just a beast,” he said, highlighting its role in real-time computer graphics and AI processing. Driving the Future: NVIDIA's Three-Computer Model for Autonomous Vehicles Huang made important announcements in the area of automotive AI, highlighting NVIDIA's commitment to the automotive industry. He stated: "The autonomous vehicle revolution has arrived. After so many years, with Waymo's success and Tesla's success, it is very, very clear autonomous vehicles have finally arrived.' He predicted that the autonomous vehicle industry is likely to become the first multi-trillion-dollar industry. Huang presented NVIDIA's approach to automotive AI with a 'three-computer' model required to create autonomous vehicles: DGX for training AI models – where AI is trained on huge data sets.
Omniverse for modelling and generating synthetic data – allows you to create digital twins and test scenarios that simulate real driving conditions.
DRIVE AGX, an automotive supercomputer, is designed to deploy AI models for real-time decision-making in cars. Thor: The Next-Gen Automotive Processor A next-generation processor for cars called Thor was announced at CES 2025, which is already in mass production and offers 20 times the processing power of the previous generation Orin. Not only is Thor designed for autonomous vehicles, but it is also a general-purpose robotics computer suitable for a variety of applications, from AMR (autonomous mobile robots) to humanoids. Safety First: NVIDIA Drive OS Meets Highest Safety Standards Huang also highlighted the certification of NVIDIA Drive OS to the highest automotive safety standard, ISO 26262 ASIL-D, which is a significant milestone in automotive safety and AI integration, proudly stating: Drive OS is now the first software-defined programmable AI computer that has been certified up to ASIL D, which is the highest standard of functional safety for automobiles. Partnerships with major automakers such as Toyota, Mercedes, BYD and others were highlighted, demonstrating NVIDIA's integral role in the automotive sector. Toyota was highlighted as a new partner in developing next-generation autonomous vehicles. NVIDIA Cosmos: Understanding the Physical World He also introduced NVIDIA Cosmos, a fundamental model designed to understand the physical world. The model, trained on 20 million hours of video of dynamic physical actions, aims to revolutionize robotics and industrial AI by providing a framework for understanding physical dynamics, with applications ranging from robotics to autonomous vehicles (NVIDIA at CES 2025). Democratizing AI with GeForce Huang emphasized the democratization of AI with NVIDIA hardware, making it accessible to gamers, creators, and enterprises. He said: 'GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,' highlighting NVIDIA's role in the spread of AI. The presentation delved into the evolution of AI from perception to generative capabilities, which are now moving towards 'agent AI' and 'physical AI'. Huang discussed the three laws of scaling that guide the development of AI—scaling before learning, after learning, and during testing—and how they help develop AI capabilities. For example, the law of scaling after learning uses techniques such as reinforcement learning and human feedback to improve AI models. Robotics Reimagined: NVIDIA's Isaac Groot Platform In addition, he introduced NVIDIA's Isaac Groot platform for developing general-purpose robot models, highlighting its use for creating digital twins for robots. “The ChatGPT moment for general robotics is just around the corner," Huang predicted, emphasizing the role of modeling and synthetic data in robotics (NVIDIA Isaac Groot). In conclusion, NVIDIA’s groundbreaking innovations unveiled at CES 2025 mark a bold step toward an AI-driven future, from cigarette pack-sized supercomputers to the evolution of autonomous vehicles. Whether you’re a developer, researcher, or tech enthusiast, these advancements open up new frontiers in computing, robotics, and beyond. Want to stay ahead of the curve? Join the conversation in the comments to discuss how NVIDIA’s latest technologies can transform your industry or project! Speaking at CES on 6 January 2025, Jensen Huang, CEO of NVIDIA, delivered the keynote address from the digitally recreated NVIDIA headquarters, painting a fascinating picture of how artificial intelligence is making impact across industries and everyday life. CES AI in Your Pocket: Introducing NVIDIA Project DIGITS One of the most intriguing announcements was NVIDIA's Project DIGITS, a new AI-powered personal supercomputer. Huang was impressed by its compact design, similar to the 'size of a pack of cigarettes', but capable of delivering a petaflop of computing power. This innovation aims to democratize AI supercomputing by making it accessible to small labs, lone professionals, and even home users ( NVIDIA Project Digits ). petaflop NVIDIA Project Digits Consider a scenario where researchers have access to significant computational resources directly from their workstations. This eliminates dependencies on cloud computing or traditional supercomputers, which can be limited by availability and cost. In this setting, a scientist working in a smaller lab could conduct their research with greater autonomy, utilizing a powerful, localized supercomputer. This accessibility to high-performance computing would enable individual scientists and smaller teams within startups to engage in AI research more readily. This shift could lead to increased research and development activities, as barriers to computational resources are reduced. Data analysis and model training could be performed more quickly and iteratively. Consequently, this future could see more individuals and organizations participating in scientific and technological AI advancements. From Drug Discovery to Climate Solutions: The Power of AI Tokens "This is how intelligence is created,” Huang said, introducing the new era of AI development. He explained how AI tokens, the building blocks of intelligent systems, open up endless possibilities. Imagine a future in which AI-assisted drug discovery accelerates the development of life-saving medicines, climate change is mitigated through energy optimization and renewable energy solutions, and personalized education is made available to all as AI tutors adapt to individual learning styles. The RTX 50 Series Unveiled Moving on to hardware, Huang introduced the RTX 50 series, a new powerful GPU designed to accelerate AI workloads. With 92 billion transistors and offering 4,000 TOPS (terabytes of operations per second) for AI, it is three times more powerful than its predecessor, the Ada generation. Huang emphasized its usefulness in both gaming and professional applications: “This RTX 50 Series, Blackwell architecture... the GPU is just a beast,” he said, highlighting its role in real-time computer graphics and AI processing. Driving the Future: NVIDIA's Three-Computer Model for Autonomous Vehicles Huang made important announcements in the area of automotive AI, highlighting NVIDIA's commitment to the automotive industry. He stated: "The autonomous vehicle revolution has arrived. After so many years, with Waymo's success and Tesla's success, it is very, very clear autonomous vehicles have finally arrived.' He predicted that the autonomous vehicle industry is likely to become the first multi-trillion-dollar industry. Huang presented NVIDIA's approach to automotive AI with a 'three-computer' model required to create autonomous vehicles: DGX for training AI models – where AI is trained on huge data sets. Omniverse for modelling and generating synthetic data – allows you to create digital twins and test scenarios that simulate real driving conditions. DRIVE AGX, an automotive supercomputer, is designed to deploy AI models for real-time decision-making in cars. DGX for training AI models – where AI is trained on huge data sets. DGX for training AI models Omniverse for modelling and generating synthetic data – allows you to create digital twins and test scenarios that simulate real driving conditions. Omniverse for modelling and generating synthetic data DRIVE AGX, an automotive supercomputer , is designed to deploy AI models for real-time decision-making in cars. DRIVE AGX, an automotive supercomputer Thor: The Next-Gen Automotive Processor A next-generation processor for cars called Thor was announced at CES 2025, which is already in mass production and offers 20 times the processing power of the previous generation Orin. Not only is Thor designed for autonomous vehicles, but it is also a general-purpose robotics computer suitable for a variety of applications, from AMR (autonomous mobile robots) to humanoids. Safety First: NVIDIA Drive OS Meets Highest Safety Standards Huang also highlighted the certification of NVIDIA Drive OS to the highest automotive safety standard, ISO 26262 ASIL-D, which is a significant milestone in automotive safety and AI integration, proudly stating: Drive OS is now the first software-defined programmable AI computer that has been certified up to ASIL D, which is the highest standard of functional safety for automobiles. Drive OS is now the first software-defined programmable AI computer that has been certified up to ASIL D, which is the highest standard of functional safety for automobiles. Partnerships with major automakers such as Toyota, Mercedes, BYD and others were highlighted, demonstrating NVIDIA's integral role in the automotive sector. Toyota was highlighted as a new partner in developing next-generation autonomous vehicles. NVIDIA Cosmos: Understanding the Physical World He also introduced NVIDIA Cosmos, a fundamental model designed to understand the physical world. The model, trained on 20 million hours of video of dynamic physical actions, aims to revolutionize robotics and industrial AI by providing a framework for understanding physical dynamics, with applications ranging from robotics to autonomous vehicles ( NVIDIA at CES 2025 ). NVIDIA at CES 2025 Democratizing AI with GeForce Huang emphasized the democratization of AI with NVIDIA hardware, making it accessible to gamers, creators, and enterprises. He said: 'GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,' highlighting NVIDIA's role in the spread of AI. The presentation delved into the evolution of AI from perception to generative capabilities, which are now moving towards ' agent AI' and 'physical AI'. Huang discussed the three laws of scaling that guide the development of AI—scaling before learning, after learning, and during testing—and how they help develop AI capabilities. For example, the law of scaling after learning uses techniques such as reinforcement learning and human feedback to improve AI models. agent AI' Robotics Reimagined: NVIDIA's Isaac Groot Platform In addition, he introduced NVIDIA's Isaac Groot platform for developing general-purpose robot models, highlighting its use for creating digital twins for robots. “The ChatGPT moment for general robotics is just around the corner," Huang predicted, emphasizing the role of modeling and synthetic data in robotics ( NVIDIA Isaac Groot ). NVIDIA Isaac Groot In conclusion, NVIDIA’s groundbreaking innovations unveiled at CES 2025 mark a bold step toward an AI-driven future, from cigarette pack-sized supercomputers to the evolution of autonomous vehicles. Whether you’re a developer, researcher, or tech enthusiast, these advancements open up new frontiers in computing, robotics, and beyond. Want to stay ahead of the curve? Join the conversation in the comments to discuss how NVIDIA’s latest technologies can transform your industry or project! Want to stay ahead of the curve? Join the conversation in the comments to discuss how NVIDIA’s latest technologies can transform your industry or project!

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