ENISA Hosts 9th eHealth Security Conference in Budapest to Tackle Cybersecurity Challenges in Healthcare sector.

ENISA hosts 9th eHealth security conference to tackle cybersecurity challenges in healthcare - Industrial Cyber

CLEVELAND — Trucking is dealing with an evolving cybersecurity environment that raised concerns among industry experts during a panel discussion Oct. 28.

Trucking Stakeholders Tackle Biggest Cybersecurity Concerns | Transport Topics

Okta and the OpenID Foundation today announced the formation of an OpenID Foundation working group with Ping Identity, Microsoft, SGNL, and Beyond Ide

Okta, OpenID Foundation & Tech Firms Tackle Today’s Biggest Cybersecurity Challenge: Identity Security in SaaS Apps | Business Wire

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Authors:
(1) Oleksandr Kuznetsov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA and Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, 62100 Macerata, Italy (kuznetsov@karazin.ua);
(2) Dzianis Kanonik, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA;
(3) Alex Rusnak, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA (alex@proxima.one);
(4) Anton Yezhov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA;
(5) Oleksandr Domin, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA. Table of Links Abstract and 1. Introduction 1.1. The Blockchain Paradigm and the Challenge of Scalability 1.2. State of the art 1.3. Our contribution and 1.4. Article structure 2. Conceptualizing the Problem 3. Our Idea for Optimizing Trees in Blockchain 4. Efficiency of adaptive Merkle trees 5. Algorithm for Merkle Tree Restructuring 6. Examples of Merkle Tree Restructuring Algorithm Execution and 6.1 Example 1: Restructuring a Binary Tree by Adding One Leaf 6.2. Example 1.1: Binary Tree Restructuring Through Leaf Node Swapping 6.3. Example 2.1: Restructuring a Non-Binary Tree by Adding a Single Leaf 6.4. Example 2.2: Restructuring a Non-Binary Tree Through Leaf Pair Swapping 6.5. Example 2.3: Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping 7. Path Encoding in the Adaptive Merkle Tree 8. Enhancing Verkle Trees Through Adaptive Restructuring and 8.1. Application of Adaptive Trees in Verkle Tree Technology 8.2. Technology and Advantages 9. Discussion 9.1. Our Contribution 9.2. Comparison with Existing Solutions 10. Conclusion and References 9. Discussion In this work, we have embarked on a comprehensive exploration of optimizing tree structures within the blockchain ecosystem, addressing the critical challenge of scalability that plagues current blockchain technologies. Our investigation spans from conceptualizing the inherent problems associated with traditional Merkle trees to proposing and validating an innovative approach for adaptive restructuring of these trees to enhance efficiency and scalability in blockchain systems. The blockchain paradigm, while revolutionary, faces significant scalability challenges, primarily due to the inherent limitations of its underlying data structures and consensus mechanisms. Traditional Merkle trees, despite their widespread adoption for ensuring data integrity and facilitating efficient verifications, contribute to these scalability issues due to their static nature and the increasing cost of operations as the blockchain grows. Existing solutions to blockchain scalability, such as sharding and layer 2 protocols, offer partial remedies by distributing the workload or offloading transactions. However, these approaches often introduce complexity or compromise on decentralization and security. Our review of the state of the art highlights a gap in dynamically optimizing the data structures themselves to directly address the root causes of inefficiency. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. Authors: (1) Oleksandr Kuznetsov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA and Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, 62100 Macerata, Italy (kuznetsov@karazin.ua); (2) Dzianis Kanonik, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA; (3) Alex Rusnak, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA (alex@proxima.one); (4) Anton Yezhov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA; (5) Oleksandr Domin, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA. Authors: Authors: (1) Oleksandr Kuznetsov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA and Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 30/32, 62100 Macerata, Italy (kuznetsov@karazin.ua); (2) Dzianis Kanonik, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA; (3) Alex Rusnak, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA (alex@proxima.one); (4) Anton Yezhov, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA; (5) Oleksandr Domin, Proxima Labs, 1501 Larkin Street, suite 300, San Francisco, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 1.1. The Blockchain Paradigm and the Challenge of Scalability 1.1. The Blockchain Paradigm and the Challenge of Scalability 1.2. State of the art 1.2. State of the art 1.3. Our contribution and 1.4. Article structure 1.3. Our contribution and 1.4. Article structure 2. Conceptualizing the Problem 2. Conceptualizing the Problem 3. Our Idea for Optimizing Trees in Blockchain 3. Our Idea for Optimizing Trees in Blockchain 4. Efficiency of adaptive Merkle trees 4. Efficiency of adaptive Merkle trees 5. Algorithm for Merkle Tree Restructuring 5. Algorithm for Merkle Tree Restructuring 6. Examples of Merkle Tree Restructuring Algorithm Execution and 6.1 Example 1: Restructuring a Binary Tree by Adding One Leaf 6. Examples of Merkle Tree Restructuring Algorithm Execution and 6.1 Example 1: Restructuring a Binary Tree by Adding One Leaf 6.2. Example 1.1: Binary Tree Restructuring Through Leaf Node Swapping 6.2. Example 1.1: Binary Tree Restructuring Through Leaf Node Swapping 6.3. Example 2.1: Restructuring a Non-Binary Tree by Adding a Single Leaf 6.3. Example 2.1: Restructuring a Non-Binary Tree by Adding a Single Leaf 6.4. Example 2.2: Restructuring a Non-Binary Tree Through Leaf Pair Swapping 6.4. Example 2.2: Restructuring a Non-Binary Tree Through Leaf Pair Swapping 6.5. Example 2.3: Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping 6.5. Example 2.3: Restructuring a Patricia-Merkle Tree Fragment Through Leaf Pair Swapping 7. Path Encoding in the Adaptive Merkle Tree 7. Path Encoding in the Adaptive Merkle Tree 8. Enhancing Verkle Trees Through Adaptive Restructuring and 8.1. Application of Adaptive Trees in Verkle Tree Technology 8. Enhancing Verkle Trees Through Adaptive Restructuring and 8.1. Application of Adaptive Trees in Verkle Tree Technology 8.2. Technology and Advantages 8.2. Technology and Advantages 9. Discussion 9. Discussion 9.1. Our Contribution 9.1. Our Contribution 9.2. Comparison with Existing Solutions 9.2. Comparison with Existing Solutions 10. Conclusion and References 10. Conclusion and References 9. Discussion In this work, we have embarked on a comprehensive exploration of optimizing tree structures within the blockchain ecosystem, addressing the critical challenge of scalability that plagues current blockchain technologies. Our investigation spans from conceptualizing the inherent problems associated with traditional Merkle trees to proposing and validating an innovative approach for adaptive restructuring of these trees to enhance efficiency and scalability in blockchain systems. The blockchain paradigm, while revolutionary, faces significant scalability challenges, primarily due to the inherent limitations of its underlying data structures and consensus mechanisms. Traditional Merkle trees, despite their widespread adoption for ensuring data integrity and facilitating efficient verifications, contribute to these scalability issues due to their static nature and the increasing cost of operations as the blockchain grows. Existing solutions to blockchain scalability, such as sharding and layer 2 protocols, offer partial remedies by distributing the workload or offloading transactions. However, these approaches often introduce complexity or compromise on decentralization and security. Our review of the state of the art highlights a gap in dynamically optimizing the data structures themselves to directly address the root causes of inefficiency. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv

How Adaptive Tree Structures Tackle Blockchain Scalability Issues

How Our Disentangled Learning Framework Tackles Lifelong Learning Challenges

Authors:
(1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de);
(2) Çagatay Yıldız, University of Tübingen;
(3) Gido M. van de Ven, KU Leuven;
(4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox;
(5) Tinne Tuytelaars, KU Leuven;
(6) Matthias Bethge, University of Tübingen. Table of Links Abstract and 1. Introduction 2. Two problems with the current approach to class-incremental continual learning 3. Methods and 3.1. Infinite dSprites 3.2. Disentangled learning 4. Related work 4.1. Continual learning and 4.2. Benchmarking continual learning 5. Experiments 5.1. Regularization methods and 5.2. Replay-based methods 5.3. Do we need equivariance? 5.4. One-shot generalization and 5.5. Open-set classification 5.6. Online vs. offline Conclusion, Acknowledgments and References Supplementary Material 3.2. Disentangled learning With our procedural benchmark generator, we can test continual learning methods over time frames an order of magnitude longer than those covered by existing datasets. As previously mentioned, we hypothesize that to learn efficiently over such time horizons, we need to clearly distinguish between the generalization mechanism that needs to be learned and the class-specific information that has to be memorized. We start by observing that human learning is likely characterized by such separation. Take face recognition, for example. A child is able to memorize the face of its parent but can still get confused by an unexpected transformation, as evidenced by countless online videos of babies failing to recognize their fathers after a shave. Once we learn the typical identity preserving transformations that a face can undergo, we need only to memorize the particular features of any new face to instantly generalize over many transformations, such as facial expression, lighting, three-dimensional rotation, scale, perspective projection, or a change of hairstyle. Note that while we encounter new faces every day, these transforms remain consistent and affect every face similarly. Inspired by this observation, we aim to disentangle generalization from memorization by explicitly separating the learning module from the memory buffer in our model design. The memory buffer stores a single exemplar image of each encountered shape. We assume these are given to us by an oracle throughout training, but it would be possible to bootstrap the buffer with a few initial exemplars. The equivariance learning module is a neural network designed to learn the general transformations present in the data. Discussion The disentangled learning approach has a number of advantages. First, by learning transformations instead of class boundaries, we reformulate a challenging class-incremental classification scenario as a domain-incremental FoV regression learning problem [37]. Since the transformations affect every class in the same way, they are easier to learn in a continual setting. We show that this approach is not only less prone to forgetting but exhibits significant forward and backward transfer. In other words, the knowledge about regressing FoVs is efficiently accumulated over time. Second, the exemplar buffer is a fully explainable representation of memory that can be explicitly edited: we can easily add a new class or completely erase a class from memory by removing its exemplar. Finally, we show experimentally that our method generalises instantly to new shapes with just a single exemplar and works reliably in an open-set classification scenario. Figure 3 illustrates the stages of the classification mechanism: five input images, their corresponding normalization network outputs, and closest exemplars from the buffer. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de); (2) Çagatay Yıldız, University of Tübingen; (3) Gido M. van de Ven, KU Leuven; (4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox; (5) Tinne Tuytelaars, KU Leuven; (6) Matthias Bethge, University of Tübingen. Authors: Authors: (1) Sebastian Dziadzio, University of Tübingen (sebastian.dziadzio@uni-tuebingen.de); (2) Çagatay Yıldız, University of Tübingen; (3) Gido M. van de Ven, KU Leuven; (4) Tomasz Trzcinski, IDEAS NCBR, Warsaw University of Technology, Tooploox; (5) Tinne Tuytelaars, KU Leuven; (6) Matthias Bethge, University of Tübingen. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Two problems with the current approach to class-incremental continual learning 2. Two problems with the current approach to class-incremental continual learning 3. Methods and 3.1. Infinite dSprites 3. Methods and 3.1. Infinite dSprites 3.2. Disentangled learning 3.2. Disentangled learning 4. Related work 4. Related work 4.1. Continual learning and 4.2. Benchmarking continual learning 4.1. Continual learning and 4.2. Benchmarking continual learning 5. Experiments 5. Experiments 5.1. Regularization methods and 5.2. Replay-based methods 5.1. Regularization methods and 5.2. Replay-based methods 5.3. Do we need equivariance? 5.3. Do we need equivariance? 5.4. One-shot generalization and 5.5. Open-set classification 5.4. One-shot generalization and 5.5. Open-set classification 5.6. Online vs. offline 5.6. Online vs. offline Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References Supplementary Material Supplementary Material 3.2. Disentangled learning With our procedural benchmark generator, we can test continual learning methods over time frames an order of magnitude longer than those covered by existing datasets. As previously mentioned, we hypothesize that to learn efficiently over such time horizons, we need to clearly distinguish between the generalization mechanism that needs to be learned and the class-specific information that has to be memorized. We start by observing that human learning is likely characterized by such separation. Take face recognition, for example. A child is able to memorize the face of its parent but can still get confused by an unexpected transformation, as evidenced by countless online videos of babies failing to recognize their fathers after a shave. Once we learn the typical identity preserving transformations that a face can undergo, we need only to memorize the particular features of any new face to instantly generalize over many transformations, such as facial expression, lighting, three-dimensional rotation, scale, perspective projection, or a change of hairstyle. Note that while we encounter new faces every day, these transforms remain consistent and affect every face similarly. Inspired by this observation, we aim to disentangle generalization from memorization by explicitly separating the learning module from the memory buffer in our model design. The memory buffer stores a single exemplar image of each encountered shape. We assume these are given to us by an oracle throughout training, but it would be possible to bootstrap the buffer with a few initial exemplars. The equivariance learning module is a neural network designed to learn the general transformations present in the data. Discussion The disentangled learning approach has a number of advantages. First, by learning transformations instead of class boundaries, we reformulate a challenging class-incremental classification scenario as a domain-incremental FoV regression learning problem [37]. Since the transformations affect every class in the same way, they are easier to learn in a continual setting. We show that this approach is not only less prone to forgetting but exhibits significant forward and backward transfer. In other words, the knowledge about regressing FoVs is efficiently accumulated over time. Second, the exemplar buffer is a fully explainable representation of memory that can be explicitly edited: we can easily add a new class or completely erase a class from memory by removing its exemplar. Finally, we show experimentally that our method generalises instantly to new shapes with just a single exemplar and works reliably in an open-set classification scenario. Figure 3 illustrates the stages of the classification mechanism: five input images, their corresponding normalization network outputs, and closest exemplars from the buffer. Discussion 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

How Disentangled Learning Tackles Catastrophic Forgetting

Authors:
(1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu);
(2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com);
(3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Related Work Project Prerequisites Project Implementation Insight and Reflections Conclusions, Acknowledgement and References 2 RELATED WORK Much has been written about the advantages of introducing version control with Git and GitHub in the classroom [4, 5, 6]. The concept of test-driven learning, which relates to the software engineering concept of test-driven-development and advocates for demonstrating the use of automated tests alongside teaching programming concepts early in students’ computer science education, is also found in the literature [7, 8]. Our experience corroborates the findings in the literature that an early introduction to version control and automated testing is advantageous but demonstrates a unique experience where the concepts are presented in a research lab setting that culminates with students submitting pull requests to deploy their automated tests to real open-source projects. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Authors: Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Project Prerequisites Project Prerequisites Project Implementation Project Implementation Insight and Reflections Insight and Reflections Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References 2 RELATED WORK Much has been written about the advantages of introducing version control with Git and GitHub in the classroom [4, 5, 6]. The concept of test-driven learning, which relates to the software engineering concept of test-driven-development and advocates for demonstrating the use of automated tests alongside teaching programming concepts early in students’ computer science education, is also found in the literature [7, 8]. Our experience corroborates the findings in the literature that an early introduction to version control and automated testing is advantageous but demonstrates a unique experience where the concepts are presented in a research lab setting that culminates with students submitting pull requests to deploy their automated tests to real open-source projects. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

High School Students Tackle Automated Testing and Version Control

DePIN Tackles the World’s Most Entrenched Industries

DePIN বিশ্বের সবচেয়ে প্রবেশ করা শিল্পকে মোকাবেলা করে

DePIN nimmt sich der am stärksten verankerten Branchen der Welt an

DePIN aborda las industrias más arraigadas del mundo

DePIN s’attaque aux industries les plus implantées au monde

DePIN दुनिया के सबसे अधिक स्थापित उद्योगों से निपटता है

DePINは世界で最も定着している産業に取り組む

DePIN은 세계에서 가장 확고한 산업을 다루고 있습니다

DePIN aborda as indústrias mais arraigadas do mundo

DePIN решает проблемы самых укоренившихся в мире отраслей

DePIN Dünyanın En Köklü Endüstrileriyle Mücadele Ediyor

DePIN giải quyết các ngành công nghiệp cố thủ nhất thế giới

DePIN 应对全球最根深蒂固的行业

WOMBO, the makers of viral AI avatar apps, and io.net, a pioneer in decentralized GPU compute networks, have just struck a partnership that represents a major step towards expanding the possibilities of AI computing. This collaboration comes at a time when artificial intelligence (AI) is pushing the boundaries of technology. This agreement marks a significant step forward in the development of artificial intelligence's computing capabilities. This cooperation not only represents a milestone in the development of artificial intelligence-powered applications, but it also addresses the issue of restricted processing capacity that many AI startups are now confronting. It is anticipated that over 200 million copies of WOMBO's applications have been downloaded from the Google App Store and for iOS. The firm's most popular and successful apps are WOMBO, Dream, and WOMBO Me. Because they need a significant quantity of processing power, they have found themselves drawn to io.net's cutting-edge decentralized GPU computing network. Io.net emerges as a pioneering force, offering decentralized, geo-distributed computing networks with the potential to transform the artificial intelligence infrastructure. Io.net is a pioneering organization. The ability of WOMBO to employ Apple silicon chip clusters as fuel for its machine learning (ML) models is critical to the success of this collaboration. This would help artificial intelligence enterprises overcome the challenges of cloud computing, such as high cost and limited accessibility. Partnership Goals and Technology The partnership between WOMBO and io.net comes against the background of the AI industry's battle with high cloud computing operational costs and a restricted supply of hardware. The novel technology developed by Io.net aggregates decentralized and geographically dispersed GPUs, enabling clusters to be deployed on demand for a fraction of the typical cost. This strategy has opened up new avenues of opportunity for WOMBO, allowing it to grow its AI applications more effectively and efficiently. WOMBO CEO Ben-Zion Benkhin expressed his enthusiasm for the partnership, stating, “We are excited about partnering with io.net to help bring unused computing power and put it to use in groundbreaking AI applications - together, our teams have the potential to put a serious dent in the GPU supply shortage.” This sentiment captures the collaborative spirit and the ambitious vision shared by both companies to overcome the challenges of GPU supply constraints. Tapping into Apple’s Silicon Chips Central to this strategic alliance is an innovative endeavor that utilizes the extraordinary capabilities of Apple silicon chip clusters. This initiative is poised to leverage the Neural Engine capabilities on Apple’s chips along with io.net’s mega-clustering capabilities, tapping into hundreds of millions of consumer devices for AI workloads. This approach not only showcases the technical synergy between WOMBO and io.net but also highlights the potential for scalable AI application development on a global scale. Final Thoughts Io.net is a decentralized physical infrastructure network (DePIN) that develops and administers decentralized GPU clusters produced on demand from sources distributed throughout the globe. Through its infrastructure of over 520 thousand graphics processing units (GPUs) and central processing units (CPUs), io.net democratizes access to GPU computing power, lowering prices and extending opportunities for both engineers and enterprises. This network has been optimized for use cases that need high processing demand and low latency, such as artificial intelligence and machine learning activities, as well as cloud gaming, by inventing a new compute network standard. In terms of artificial intelligence, the partnership between WOMBO and io.net represents a change towards computer solutions that are more environmentally friendly, accessible, and efficient. This cooperation, which employs distributed computing and the power of Apple silicon processors, not only prepares the path for WOMBO's future growth but also sets a pattern for how artificial intelligence firms might overcome limitations in computing capacity and cost. 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. WOMBO, the makers of viral AI avatar apps, and io.net, a pioneer in decentralized GPU compute networks, have just struck a partnership that represents a major step towards expanding the possibilities of AI computing. This collaboration comes at a time when artificial intelligence (AI) is pushing the boundaries of technology. This agreement marks a significant step forward in the development of artificial intelligence's computing capabilities. This cooperation not only represents a milestone in the development of artificial intelligence-powered applications, but it also addresses the issue of restricted processing capacity that many AI startups are now confronting. It is anticipated that over 200 million copies of WOMBO's applications have been downloaded from the Google App Store and for iOS. The firm's most popular and successful apps are WOMBO, Dream, and WOMBO Me. Because they need a significant quantity of processing power, they have found themselves drawn to io.net's cutting-edge decentralized GPU computing network. Io.net emerges as a pioneering force, offering decentralized, geo-distributed computing networks with the potential to transform the artificial intelligence infrastructure. Io.net is a pioneering organization. The ability of WOMBO to employ Apple silicon chip clusters as fuel for its machine learning (ML) models is critical to the success of this collaboration. This would help artificial intelligence enterprises overcome the challenges of cloud computing, such as high cost and limited accessibility. Partnership Goals and Technology The partnership between WOMBO and io.net comes against the background of the AI industry's battle with high cloud computing operational costs and a restricted supply of hardware. The novel technology developed by Io.net aggregates decentralized and geographically dispersed GPUs, enabling clusters to be deployed on demand for a fraction of the typical cost. This strategy has opened up new avenues of opportunity for WOMBO, allowing it to grow its AI applications more effectively and efficiently. WOMBO CEO Ben-Zion Benkhin expressed his enthusiasm for the partnership, stating, “We are excited about partnering with io.net to help bring unused computing power and put it to use in groundbreaking AI applications - together, our teams have the potential to put a serious dent in the GPU supply shortage.” This sentiment captures the collaborative spirit and the ambitious vision shared by both companies to overcome the challenges of GPU supply constraints. Tapping into Apple’s Silicon Chips Central to this strategic alliance is an innovative endeavor that utilizes the extraordinary capabilities of Apple silicon chip clusters. This initiative is poised to leverage the Neural Engine capabilities on Apple’s chips along with io.net’s mega-clustering capabilities, tapping into hundreds of millions of consumer devices for AI workloads. This approach not only showcases the technical synergy between WOMBO and io.net but also highlights the potential for scalable AI application development on a global scale. Final Thoughts Io.net is a decentralized physical infrastructure network (DePIN) that develops and administers decentralized GPU clusters produced on demand from sources distributed throughout the globe. Through its infrastructure of over 520 thousand graphics processing units (GPUs) and central processing units (CPUs), io.net democratizes access to GPU computing power, lowering prices and extending opportunities for both engineers and enterprises. This network has been optimized for use cases that need high processing demand and low latency, such as artificial intelligence and machine learning activities, as well as cloud gaming, by inventing a new compute network standard. In terms of artificial intelligence, the partnership between WOMBO and io.net represents a change towards computer solutions that are more environmentally friendly, accessible, and efficient. This cooperation, which employs distributed computing and the power of Apple silicon processors, not only prepares the path for WOMBO's future growth but also sets a pattern for how artificial intelligence firms might overcome limitations in computing capacity and cost. 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

Breaking Barriers in AI Development: WOMBO and io.net Unite to Tackle GPU Shortage

The presentation below is from Mikhail Lazarev, CTO at Gearbox Protocol. He presented the use cases of Gearbox Protocol in solving challenges with oracles. https://youtu.be/I46OGQDf6E4?embedable=true Below is a glossary of key concepts mentioned during Mikhail’s talk, intended as a supplement to his video presentation. GEARBOX PROTOCOL brings you onchain credit, allowing anyone to margin trade on Uniswap, leverage farm on Curve, leverage stake on Lido, and use 10X more capital on many DeFi protocols you love. Making decentralized leverage a reality thanks to Credit Account abstraction! Gearbox Protocol Gearbox Protocol uses Credit Account abstraction to bring together lending and prime brokerage in the same protocol. Lenders deposit assets to earn passive yield, while the composable leverage side users borrow these assets to create spot leverage positions, which can be used across DeFi. That could be margin trading on Uniswap, farming on Curve and Balancer, leverage staking on Lido and Rocketpool, and a lot more. All of that is made possible with Gearbox’s innovative Credit Account abstraction, creating the base layer of DeFi leverage. CREDIT ACCOUNTS Gearbox uses Credit Accounts which are isolated smart contracts with predetermined parameters. Credit Accounts encompass both the user’s funds and the borrowed margin funds. They can be deployed on any DeFi protocol and are designed to be non-custodial, ensuring that users retain ownership of their assets at all times. Users can close the account by paying off the debt or withdrawing any fund remaining. Lenders in this protocol benefit by getting high additional yield and risk tolerance. ORACLES Blockchain oracles act as bridges between the blockchain and external data sources, such as market prices, weather data, sports scores, and more. They collect data from these sources and make it available to smart contracts on the blockchain, allowing these contracts to react to real-world events and conditions. Oracles play a crucial role in enabling blockchain-based applications to interact with the broader world and expand their utility. Oracles can also help to transfer data from one isolated blockchain environment to another, thus enabling cross-chain communication. is one example of an oracle network. It is an open-source protocol with a and runs on the Ethereum Network. Chainlink recently introduced an interoperability protocol, , which is used for cross-chain transactions. Chainlink proof-of-stake consensus mechanism CCIP PRICE ORACLE ATTACK is a form of cybercrime where attackers use flash loans to exploit blockchains due to vulnerabilities in smart contracts. It is one of the most common attack methods in DeFi. Price oracle attack allow users to borrow funds without the use of collateral. In a flash loan scenario, the borrower is required to repay the loan within the same transaction, or else the smart contract will automatically reverse the entire transaction. Thus, the fund is reverted automatically if the loan cannot be repaid. Flash loans Flash loans can be used to artificially move a token’s price, allowing the attacker to exploit the anomalous pricing. Dive into more details of past flash loan attacks and how to prevent them in . this article CURVE LP TOKENS Curve liquidity pools are reserves of tokens on smart contracts in Curve Finance. They are maintained by liquidity providers that stake their assets and earn Annual Percentage Yield (APY) in return, depending on the volume of transactions. by Curve Founder, Michael Egorov, provides full details on the inner workings of the protocol. This post PRICE FEED A is a mechanism used to source price data of assets. They usually aggregate prices from several sources. They may also relay updates to the blockchain when prices change or a certain period has elapsed. price feed Popular price oracles include Chainlink Price Feeds, Compound’s Open Price Feed, and Uniswaps TWAP. Recently DeFi Wonderland have  released Price Oracle, in order to provide a truly permissionless and decentralized solution for price feeds. stETH (stETH) is a token that was created to represent Ether (ETH) that has been “staked” or deposited onto the DeFi platform, Lido Finance. When users stake ETH on Lido, they get the derivative, stETH, which can be used to perform the regular activities of Ether. Staked ETH on l is known as rETH. More information about the effects of staked ether can be found . Lido Staked Ether Rocketpoo here Also published here. Lead image by on Unsplash Shubham Dhage

How Gearbox Protocol Tackles Oracle Challenges in Defi 

কিভাবে গিয়ারবক্স প্রোটোকল ডিফিতে ওরাকল চ্যালেঞ্জ মোকাবেলা করে

Wie Gearbox Protocol Oracle-Herausforderungen in DeFi bewältigt

Cómo el protocolo Gearbox aborda los desafíos de Oracle en Defi

Comment le protocole Gearbox relève les défis Oracle dans Defi

गियरबॉक्स प्रोटोकॉल डेफी में ओरेकल चुनौतियों से कैसे निपटता है

Gearbox プロトコルが Defi で Oracle の課題に取り組む方法

Gearbox Protocol이 Defi에서 Oracle의 과제를 해결하는 방법

Como o protocolo Gearbox enfrenta os desafios da Oracle em Defi

Как протокол Gearbox решает проблемы Oracle в Defi

Gearbox Protokolü Defi'deki Oracle Zorluklarının Üstesinden Nasıl Geliyor?

Giao thức hộp số giải quyết các thách thức của Oracle trong Defi như thế nào

Gearbox Protocol 如何应对 Defi 领域的 Oracle 挑战

You can jump to any section of y The National Cislunar Science & Technology Strateg here. Existing and future challenges that this strategy addresses The acceleration of activity in Cislunar space necessitates a cohesive and coordinated strategy to overcome early challenges associated with safe and sustainable operations in this complex spaceflight region. Achieving the U.S. government’s goal of responsible, peaceful, and sustainable activities in Cislunar space will require coordination among U.S. government and external entities (e.g. commercial, nonprofit, academic, and international actors). This strategy addresses key early scientific and technological challenges associated with spaceflight in this region of space. This content was initially published on in November 2022. whitehouse.gov This content has been broken-up into bite sized sections, each with unique headlines and AI-generated lead images, in line with the provisions of Creative Commons 3.0.

Pioneering the Cosmos: U.S. Strategy Tackles Early Hurdles in Cislunar Spaceflight

A Pioneering Partnership Addresses Widespread Adoption Hurdles In a landscape marked by the promise of blockchain revolutionizing industries, and Celestia have joined forces to introduce an open protocol that will revolutionize Ethereum's landscape. This collaboration aims to empower builders to launch Move-based Layer 2 (L2) blockchains and scaling solutions seamlessly integrated within the Ethereum ecosystem. Movement Labs One of the standout features is Movement's formal verification system, designed to audit and maintain secure smart contract execution across distributed environments. This not only addresses security concerns but also ensures compatibility within the native Ethereum ecosystem, reducing the steep learning curve associated with new programming languages. Overcoming Hurdles: The Widespread Adoption Challenge The of $3.8 billion in the blockchain industry due to smart contract vulnerabilities has prompted Movement and Celestia to step in with a unique approach. Rather than introducing alternative virtual machines that contribute to developer fragmentation, the partnership focuses on providing a secure, performant, and scalable modular blockchain solution using Ethereum as the foundation settlement layer. current annual loss While Labs and Celestia chart a course for Ethereum's evolution, the broader blockchain community grapples with challenges hindering widespread adoption. Although the challenges are formidable, the blockchain community is actively working to overcome them. Initiatives focusing on education, user experience, scalability solutions, and interoperability are gaining traction. Additionally, collaborations between blockchain projects and industry players are fostering innovation and addressing regulatory concerns. Movement As we navigate through these challenges, it's important to recognize that the widespread adoption of blockchain is not an overnight feat. It requires a concerted effort from developers, businesses, regulators, and the broader community. By addressing the core issues and fostering a supportive ecosystem, we can unlock the true potential of blockchain technology, ushering in a new era of transparency, efficiency, and decentralized innovation. The journey toward widespread adoption may be challenging, but the destination holds the promise of a more inclusive and decentralized digital future. Decoding the Movement Architecture: Modularity for Mass Appeal Central to Movement Labs' strategy is modularity, offering public goods and tooling to facilitate the widespread adoption of the Move programming language across diverse blockchain ecosystems. The upcoming M2 mainnet is poised to make history as the first Ethereum Layer Two rollup integrating Celestia's Blobstream, Snowman for Shared Sequencing, and the Move Virtual Machine for execution. The integration not only enhances performance but also minimizes resource expenditure, allowing developers to create high-performance, consumer-focused applications. The ecosystem is teeming with potential applications, from games to consumer-focused products and financial innovations that bridge the gap between traditional finance and retail. Fractal Module: Bridging the Solidity-Move Divide A game-changer within Movement's ecosystem is the Fractal module, enabling seamless conversion of Solidity scripts into opcodes understood by next-generation virtual machines like Move. This means that Solidity projects can harness the performance and security advantages of Move without requiring a single line of Move code. With over 50 partners integrating into the devnet, Movement Labs is creating a diverse ecosystem of applications and infrastructure that promises increased liquidity, consumer reach, and accessibility for teams launching on the Movement Network. Charting a Course Forward: A Unified Vision Recognizing the challenges, the blockchain community is actively working to overcome them. Initiatives focusing on education, user experience, scalability, and interoperability are gaining traction. Collaboration between blockchain projects and industry players is fostering innovation and addressing regulatory concerns. The Future for Blockchain Redefined As Movement Labs and Celestia redefine the future of Ethereum, the combined efforts of the blockchain community set the stage for a transformative era. By addressing the core issues and fostering a supportive ecosystem, the possibilities for secure, scalable, and innovative blockchain solutions seem boundless. The journey toward widespread adoption may be challenging, but the destination holds the promise of a more inclusive and decentralized digital future. In the convergence of technology and collaboration, the blockchain revolution is not just imminent; it's underway. Don’t forget to like and share the story! : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program

Unveiling Ethereum's Revolution: Movement Labs & Celestia Tackle Adoption Challenges in Blockchain 

Ethereum এর বিপ্লব উন্মোচন করা: মুভমেন্ট ল্যাবস এবং Celestia ব্লকচেইনে দত্তক নেওয়ার চ্যালেঞ্জ মোকাবেলা করে

Enthüllung der Revolution von Ethereum: Movement Labs und Celestia bewältigen Herausforderungen bei der Einführung von Blockchain

Revelando la revolución de Ethereum: Movement Labs y Celestia abordan los desafíos de adopción en Blockchain

Dévoilement de la révolution d'Ethereum : Movement Labs et Celestia relèvent les défis de l'adoption de la blockchain

एथेरियम की क्रांति का अनावरण: मूवमेंट लैब्स और सेलेस्टिया ब्लॉकचेन में अपनाने की चुनौतियों से निपटते हैं

イーサリアムの革命を明らかに: Movement Labs と Celestia がブロックチェーンの導入課題に取り組む

Ethereum의 혁명 공개: Movement Labs와 Celestia는 블록체인 채택 문제를 해결합니다.

Revelando a revolução do Ethereum: Movement Labs e Celestia enfrentam desafios de adoção em Blockchain

Представляем революцию Ethereum: Movement Labs и Celestia решают проблемы внедрения блокчейна

Ethereum'un Devrimini Açıklıyoruz: Hareket Laboratuvarları ve Celestia, Blockchain'deki Benimseme Zorluklarının Üstesinden Geliyor

Hé lộ cuộc cách mạng của Ethereum: Phòng thí nghiệm chuyển động & Celestia giải quyết các thách thức trong việc áp dụng trong Blockchain

揭开以太坊的革命：Movement Labs 和 Celestia 应对区块链采用挑战

⅔ of all venture investments by Big Energy companies go into sustainability. These include CCUS, Renewables, Hydrogen, Chemicals recycling, Grid & Utility tech, E-transport, and Digital. As pressure mounts for corporations to reduce their environmental footprint, energy giants have ramped up investments into clean energy and partnered with other firms to diversify away from oil & gas, recently announcing a pledge to be net-zero by 2050. Strategies for decarbonization are usually two-pronged: make current oil & gas operations more efficient and less carbon intensive (“core” / conventional energy) explore new forms of renewable energy generation (“new” / “future” energy) Amid the record of US oil and gas corporations, let's take a look at real examples of what these companies are doing in terms of the green transition. historical profits Hydrogen Hydrogen is poised to be a away from fossil fuels. This has been recognized by oil & gas companies as well as the Much of the existing oil & gas infrastructure (pipelines, gas turbine generators, refueling stations, etc.) can be easily repurposed for hydrogen uses, making this tech more appealing to oil & gas companies. pivotal fuel in the energy transition US Department of Energy. For example, TotalEnergies has established a partnership with Sunfire. This company produces renewable hydrogen and syngas, a mixture of hydrogen and carbon monoxide, from renewable electricity, water steam and captured CO2. Also, in August 2021, Chevron invested in Raven SR, which transforms biomass, mixed municipal solid waste, bio-solids, sewage, medical waste, and natural or biogas into renewable fuels. As for the independent players, (who do not yet partner with global giants, but have the potential to compete with them through their innovative products) we can name Electric Hydrogen (EH2), which is working on supplying ‘green hydrogen’ — hydrogen produced using renewable energy — to industrial sectors. EH2 secured $198M from a group of top investors, Fifth Wall Climate Tech led the round, with participation by S2G Ventures, and lenders Silicon Valley Bank and Trinity Capital. Carbon capture, utilization, and storage (CCUS) in mitigating the effects of climate change and reducing greenhouse gas emissions. CCUS involves capturing carbon dioxide (CO2) from various sources, utilizing it for various purposes, and safely storing it so that it does not escape into the atmosphere. The technology is critical in reducing the carbon footprint of industries such as power generation, oil refining, and cement manufacturing, and is seen as an essential component in achieving global carbon emissions reduction targets. With the increasing awareness of the need to tackle climate change, the CCUS industry is gaining traction, and investment in research and development is expected to increase in the coming years. Carbon Capture, Utilization, and Storage (CCUS) is a crucial field Under pressure from EU governments to reach its net-zero goal by 2050, European energy giant TotalEnergies introduces deep interest in carbon utilization. In early 2021, its venture arm invested in UK-based carbon recycling company Deep Branch. The company's technology generates clean, nutritious, and sustainable single-cell protein directly from carbon dioxide in industrial waste gas, enabling its clients in the feed industry to reduce their carbon footprint. In addition to these efforts, In 2020, Shell acquired Select Carbon, an Australia-based carbon recycling startup, which uses microbes to convert carbon dioxide from industrial emissions into usable products. A noteworthy independent company - NET Power - developed a power plant that burns natural gas without releasing emissions into the atmosphere. The company achieves this by storing and using CO2, instead of steam, as a way to generate electricity. , the company has raised a total of $150M investments, from Oxy Low Carbon Ventures, Exelon Corporations, the Shawn Group and 8 Rivers. According to Crunchbase Grid & utility tech The Grid & Utility tech area to the technologies and systems that make up the electrical grid and its supporting infrastructure. This includes power generation, transmission, and distribution networks, as well as energy storage solutions and demand response systems. The focus of this field is to improve the reliability, resilience, and sustainability of the grid while reducing its cost and carbon footprint. With the increasing adoption of renewable energy sources and the rise of electric vehicles, Grid & Utility tech has become an increasingly important area for investment and innovation. refers As renewable energy systems require a multi-pronged, digitized approach to ensure they stay online at all times, TotalEnergies has funded startup LevelTen Energy through its venture arm. Company’s platform connects corporate and industrial power buyers to the universe of power purchase agreements from utility-scale wind, solar, and storage projects. Beyond this, in August 2020 Shell, another industry giant, invested in Palmetto Clean Technologies - software that helps consumers understand their energy spend, while enabling and promoting the home-based use of clean energy solutions, such as solar. Chemical recycling The industry aims to convert waste plastic into valuable chemicals, hereby potentially reducing the release of harmful chemicals into the environment. Chemical recycling can also produce high-quality raw materials, thereby decreasing demand for fossil fuels and other natural resources. In contrast, the traditional method of recycling plastic, mechanical recycling, has its limitations and can only be applied to a limited number of plastic types and grades.  As technology develops, chemical recycling could promote domestic businesses and employment, creating a market for plastic waste and a new way to reuse some plastics. chemical recycling TotalEnergies has built partnerships with Citeo, chemical recycler Recycling Technologies, Nestlé, and Mars to examine the technical and economic feasibility of recycling complex plastic waste, such as small, flexible and multilayered food-grade packaging. Like other oil & gas players, In September 2021, Chevron also invested in gr3n startup. Company supplies a reliable chemical recycling solution that closes the polyethylene terephthalate (PET) lifecycle, offers polymer grade material, treats waste, lowers carbon footprints, and provides cost savings for players throughout the value chain. Renewable Energy Renewable energy sources, such as solar, wind, hydro, and bioenergy, are becoming increasingly cost-competitive with traditional fossil fuels and are expected to play a major role in meeting the world's growing energy needs in the future. , Renewables the largest source of global electricity generation by early 2025, surpassing coal. In addition, renewable energy technologies are making great strides in terms of cost-effectiveness and efficiency, with continued innovation expected to drive even greater progress in the years to come. According to the Renewables 2022 report will become Chevron’s renewable energy investment strategy has been broad, ranging from offshore wind and solar to nuclear fusion and geothermal. So, in August 2020, the industry witnessed investment from CTV to Zap Energy. It is a Seattle-based start-up company that develops and commercializes fusion energy technology using the sheared-flow stabilized (SFS) Z-pinch. As part of this, in November 2020, Shell built a partnership with Renewi and Nordsol. They will construct a bio-LNG plant in Amsterdam, where Renewi will collect organic waste, Nordsol will process it into bio-LNG, and Shell will make it available at its gas stations. ExxonMobil recently acquired a minority interest in Norway-based biofuels company Biojet, which converts forestry and wood-based construction waste into biofuels and biofuel components E-transport The transportation sector is one of the largest contributors to global greenhouse gas emissions, accounting for approximately 15% of total emissions. The adoption of EVs, which emit far less greenhouse gases than internal combustion engines, can help reduce the carbon footprint of the transportation sector and contribute to the fight against climate change. In addition to reducing emissions, EVs also offer numerous other benefits, such as improved energy efficiency, lower operating costs, and a reduced dependence on oil. The electrification of the transportation industry has also spurred innovation in battery technology and charging infrastructure, which will be crucial to making EVs a practical and convenient option for consumers. Powering cars with electricity is a growing priority for Shell. It has established partnerships with Porsche, Hyundai, and Singapore-based Energy Market Authority to develop EV tech in 2021. They will implement Southeast Asia’ first cross-border high performance charging network with 12 charge points at 6 Shell stations strategically located along Malaysia’s North-South highway. It will offer the possibility of convenient travel between Singapore, Kuala Lumpur, and Penang in the future. Similarly, TotalEnergies backed Chargetrip in 2020. The company’s platform and proprietary algorithms use multiple variables and predictive models to calculate the range of any electric vehicle, compute the best route to a destination, including the optimal charge stations in between, optimize for total travel time and travel costs. On top of that, in 2019, Chevron teamed up with EVgo. This company owns and operates a public direct current fast charging network in the U.S. Digital The digital industry has emerged as a powerful tool in promoting sustainable development, by leveraging technology to address social, economic and environmental challenges. In recent years, businesses have increasingly adopted digital solutions to enhance their operations and reduce their carbon footprint, while also improving access to information, goods and services for communities worldwide. As the digital sector continues to grow and evolve, it presents exciting opportunities for companies to develop innovative solutions and play a vital role in shaping a sustainable future. In this focus area, TotalEnergies has started partnership with supply chain and CO2 traceability startup Circulor and Recycling Technologies. Project TRACKCYCLE will embed blockchain technology into the advanced recycling value chain, with the aim of providing a fully traceable and accurately labeled record of recycled materials. In February 2022, Shell acquired Powershop, an operator of an online power company aimed to incentivize consumers in the Australian market to change their energy usage habits.It is also worth looking into an independent company WellAware. This company's technology offers end-to-end data to capture, monitor, and analyze datasets for oilfield intelligence from data collection to decision analytics. Raised $75M from investors like Activant Capital, Carlos Slim, and Dick Cheney. In conclusion, we can say that, despite some active participation from BigEnergy companies in recent years in funding the green transition (at least with venture money), we understand that still of CO2 in the world is produced from the use of fossil fuels. Therefore, we can expect even more proactive actions from those companies, especially given the recent . 92% new rules of the Net Zero Asset Owner Alliance

As Pressure Mounts, Energy Companies Turn to 7 Key Technologies to Tackle Emissions

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