Nebius, the European AI infrastructure company formerly known as Yandex, has raised $700 million in financing to power its U.S. expansion.

European AI infra company Nebius nabs $700M from Nvidia, Accel, others | TechCrunch

Nebius Group NV, the technology company rebranded from Russian internet giant Yandex, has raised $700 million from a slate of investors including Nvidia Corp. and Accel Partners LP.

Nvidia Joins $700 Million Nebius Deal For AI Cloud Services - Bloomberg

Press Release CIBC Innovation Banking announced today that it has provided an upsize to its growth capital financing facility with Jirav Inc. (Jirav). The company will use the financing to further accelerate product development,

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BBVA Spark backs Twinco Capital with €50 million debt facility

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The U.S. Federal Energy Regulatory Commission (FERC) on Thursday agreed to a revised commissioning plan for Venture Global LNG's Calcasieu Pass facility, allowing it to turn on three processing trains while work on faulty power equipment continues.

Venture Global LNG can start new units at LNG plant, says FERC

Accel

accel

Authors:
(1) Jongmin Lee, Department of Mathematical Science, Seoul National University;
(2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Abstract and 1 Introduction 1.1 Notations and preliminaries 1.2 Prior works 2 Anchored Value Iteration 2.1 Accelerated rate for Bellman consistency operator 2.2 Accelerated rate for Bellman optimality opera 3 Convergence when y=1 4 Complexity lower bound 5 Approximate Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 7 Conclusion, Acknowledgments and Disclosure of Funding and References A Preliminaries B Omitted proofs in Section 2 C Omitted proofs in Section 3 D Omitted proofs in Section 4 E Omitted proofs in Section 5 F Omitted proofs in Section 6 G Broader Impacts H Limitations 1.2 Prior works Value Iteration. Value iteration (VI) was first introduced in the DP literature [8] for finding optimal value function, and its variant approximate VI [11, 19, 30, 32, 56, 81, 90] considers approximate evaluations of the Bellman optimality operator. In RL, VI and approximate VI have served as the basis of RL algorithms such as fitted value iteration [29, 36, 50, 52, 57, 87] and temporal difference learning [41, 54, 80, 89, 94]. There is a line of research that emulates VI by learning a model of the MDP dynamics [62, 83, 85] and applying a modified Bellman operator [7, 33]. Asynchronous VI, another variation of VI updating the coordinate of value function in asynchronous manner, has also been studied in both RL and DP literature [9, 11, 88, 100]. Acceleration. Since Nesterov’s seminal work [61], there has been a large body of research on acceleration in convex minimization. Gradient descent [15] can be accelerated to efficiently reduce function value and squared gradient magnitude for smooth convex minimization problems [21, 44– 46, 60, 61, 102] and smooth strongly convex minimization problems [59, 64, 73, 86, 91]. Motivated by Nesterov acceleration, inertial fixed-point iterations [22, 42, 51, 70, 75] have also been suggested to accelerate fixed-point iterations. Anderson acceleration [2], another acceleration scheme for fixedpoint iterations, has recently been studied with interest [6, 74, 93, 101]. In DP and RL, prioritized sweeping [55] is a well-known method that changes the order of updates to accelerate convergence, and several variants [3, 18, 53, 68, 95] have been proposed. Speedy Q-learning [4] modifies the update rule of Q-learning and uses aggressive learning rates for acceleration. Recently, there has been a line of research that applies acceleration techniques of other areas to VI: [27, 28, 34, 67, 76, 79] uses Anderson acceleration of fixed-point iterations, [1, 12, 37, 38, 92] uses Nesterov acceleration of convex optimization, and [31] uses ideas inspired by PID controllers in control theory. Among those works, [1, 37, 38] applied Nesterov acceleration to obtain theoretically accelerated convergence rates, but those analyses require certain reversibility conditions or restrictions on eigenvalues of the transition probability induced by the policy. The anchor acceleration, a new acceleration mechanism distinct from Nesterov’s, lately gained attention in convex optimization and fixed-point theory. The anchoring mechanism, which retracts iterates towards the initial point, has been used to accelerate algorithms for minimax optimization and fixed-point problems [20, 43, 47, 65, 71, 78, 98, 99], and we focus on it in this paper. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Jongmin Lee, Department of Mathematical Science, Seoul National University; (2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Authors: Authors: (1) Jongmin Lee, Department of Mathematical Science, Seoul National University; (2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Notations and preliminaries 1.1 Notations and preliminaries 1.2 Prior works 1.2 Prior works 2 Anchored Value Iteration 2 Anchored Value Iteration 2.1 Accelerated rate for Bellman consistency operator 2.1 Accelerated rate for Bellman consistency operator 2.2 Accelerated rate for Bellman optimality opera 2.2 Accelerated rate for Bellman optimality opera 3 Convergence when y=1 3 Convergence when y=1 4 Complexity lower bound 4 Complexity lower bound 5 Approximate Anchored Value Iteration 5 Approximate Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 7 Conclusion, Acknowledgments and Disclosure of Funding and References 7 Conclusion, Acknowledgments and Disclosure of Funding and References A Preliminaries A Preliminaries B Omitted proofs in Section 2 B Omitted proofs in Section 2 C Omitted proofs in Section 3 C Omitted proofs in Section 3 D Omitted proofs in Section 4 D Omitted proofs in Section 4 E Omitted proofs in Section 5 E Omitted proofs in Section 5 F Omitted proofs in Section 6 F Omitted proofs in Section 6 G Broader Impacts G Broader Impacts H Limitations H Limitations 1.2 Prior works Value Iteration. Value iteration (VI) was first introduced in the DP literature [8] for finding optimal value function, and its variant approximate VI [11, 19, 30, 32, 56, 81, 90] considers approximate evaluations of the Bellman optimality operator. In RL, VI and approximate VI have served as the basis of RL algorithms such as fitted value iteration [29, 36, 50, 52, 57, 87] and temporal difference learning [41, 54, 80, 89, 94]. There is a line of research that emulates VI by learning a model of the MDP dynamics [62, 83, 85] and applying a modified Bellman operator [7, 33]. Asynchronous VI, another variation of VI updating the coordinate of value function in asynchronous manner, has also been studied in both RL and DP literature [9, 11, 88, 100]. Value Iteration. Acceleration. Since Nesterov’s seminal work [61], there has been a large body of research on acceleration in convex minimization. Gradient descent [15] can be accelerated to efficiently reduce function value and squared gradient magnitude for smooth convex minimization problems [21, 44– 46, 60, 61, 102] and smooth strongly convex minimization problems [59, 64, 73, 86, 91]. Motivated by Nesterov acceleration, inertial fixed-point iterations [22, 42, 51, 70, 75] have also been suggested to accelerate fixed-point iterations. Anderson acceleration [2], another acceleration scheme for fixedpoint iterations, has recently been studied with interest [6, 74, 93, 101]. Acceleration. In DP and RL, prioritized sweeping [55] is a well-known method that changes the order of updates to accelerate convergence, and several variants [3, 18, 53, 68, 95] have been proposed. Speedy Q-learning [4] modifies the update rule of Q-learning and uses aggressive learning rates for acceleration. Recently, there has been a line of research that applies acceleration techniques of other areas to VI: [27, 28, 34, 67, 76, 79] uses Anderson acceleration of fixed-point iterations, [1, 12, 37, 38, 92] uses Nesterov acceleration of convex optimization, and [31] uses ideas inspired by PID controllers in control theory. Among those works, [1, 37, 38] applied Nesterov acceleration to obtain theoretically accelerated convergence rates, but those analyses require certain reversibility conditions or restrictions on eigenvalues of the transition probability induced by the policy. The anchor acceleration, a new acceleration mechanism distinct from Nesterov’s, lately gained attention in convex optimization and fixed-point theory. The anchoring mechanism, which retracts iterates towards the initial point, has been used to accelerate algorithms for minimax optimization and fixed-point problems [20, 43, 47, 65, 71, 78, 98, 99], and we focus on it in this paper. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Prior Studies Have Advanced Value Iteration and Acceleration in Reinforcement Learning 

How Enterprises Can Accelerate Data Initiatives and Drive Growth in 2025

**ZUG, Switzerland, December 17th, 2024/Chainwire/--**Atom Accelerator(AADAO), a governance-mandated DAO of the Cosmos Hub, has announced $250,000 funding support to Confio for the continued development and maintenance of CosmWasm throughout 2025. This funding is being matched by Neutron, the integrated application network in Cosmos, with a further $250,000. CosmWasm, developed by Confio since 2020, powers smart contract functionality for nearly 100 chains, enabling developers to create decentralized applications (dApps) that operate seamlessly across the interchain. In the past 12 months, contracts built with CosmWasm in the Cosmos ecosystem generated over $5.7 million in fees. Despite its widespread adoption, the Interchain Foundation has thus far been the primary funder for the development and maintenance of Cosmwasm. Following organizational restructuring at the Interchain Foundation (ICF), Confio’s funding for the year 2025 has been drastically reduced. “CosmWasm is a critical resource for the Cosmos ecosystem, underpinning many of the applications and chains that drive value to the interchain,” said AADAO contributors. “This proposal represents a collaborative effort to ensure its longevity and continued impact.” The contributions form the foundation of the CosmWasm Coalition initiative aimed at raising the approximately $2 million required to maintain and develop CosmWasm in 2025. The funds will enable Confio’s team to continue delivering on critical functions, including security research and incident response, feature development and performance optimization, documentation and developer support, SDK compatibility maintenance, and testing and developer experience (DevX) tooling improvements. This initiative builds on the Cosmos Hub’s previous support for CosmWasm, including the successful allocation of $1.25 million under Proposal #103 in March 2023. The Cosmos Hub relies on CosmWasm for permissioned smart contract capabilities, making its continued maintenance essential to the Hub’s operations and future upgrades. Many consumer chains utilizing the Hub’s Interchain Security have built their infrastructure on CosmWasm, making the framework vital to sustaining and expanding the ecosystem. By committing to early funding, the Cosmos Hub secures priority consideration for its technical needs and those of its consumer chains in Confio’s development roadmap. By taking the first step in committing funding, AADAO and Neutron hope that their initiative will encourage additional contributions from other ecosystem stakeholders, ensuring the financial stability of CosmWasm and the broader interchain ecosystem. About Atom Accelerator DAO (AADAO) Atom Accelerator DAO (AADAO) is a governance-mandated DAO of the Cosmos Hub. They have been formed and mandated to drive value for the Cosmos Hub & ATOM, supporting initiatives that either directly create economic impact or produce value that can drive its growth. From this, their current primary mandates are public grants & venture grants. Atom Accelerator’s venture arm strategically invests in promising early-stage web3 founders and startups both within and outside of the Cosmos Hub’s ecosystem. As experts within the Cosmos ecosystem, Atom Accelerator offers expertise, guidance, and support while driving value toward ATOM. Website Contact Marketing & Comms Lead Mr. T Atom Accelerator DAO press@atomaccelerator.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here **ZUG, Switzerland, December 17th, 2024/Chainwire/--**Atom Accelerator(AADAO), a governance-mandated DAO of the Cosmos Hub, has announced $250,000 funding support to Confio for the continued development and maintenance of CosmWasm throughout 2025. This funding is being matched by Neutron, the integrated application network in Cosmos, with a further $250,000. CosmWasm, developed by Confio since 2020, powers smart contract functionality for nearly 100 chains, enabling developers to create decentralized applications (dApps) that operate seamlessly across the interchain. In the past 12 months, contracts built with CosmWasm in the Cosmos ecosystem generated over $5.7 million in fees. Despite its widespread adoption, the Interchain Foundation has thus far been the primary funder for the development and maintenance of Cosmwasm. Following organizational restructuring at the Interchain Foundation (ICF), Confio’s funding for the year 2025 has been drastically reduced. “CosmWasm is a critical resource for the Cosmos ecosystem, underpinning many of the applications and chains that drive value to the interchain,” said AADAO contributors. “This proposal represents a collaborative effort to ensure its longevity and continued impact.” “CosmWasm is a critical resource for the Cosmos ecosystem, underpinning many of the applications and chains that drive value to the interchain,” said AADAO contributors. “This proposal represents a collaborative effort to ensure its longevity and continued impact.” The contributions form the foundation of the CosmWasm Coalition initiative aimed at raising the approximately $2 million required to maintain and develop CosmWasm in 2025. The funds will enable Confio’s team to continue delivering on critical functions, including security research and incident response, feature development and performance optimization, documentation and developer support, SDK compatibility maintenance, and testing and developer experience (DevX) tooling improvements. This initiative builds on the Cosmos Hub’s previous support for CosmWasm, including the successful allocation of $1.25 million under Proposal #103 in March 2023. The Cosmos Hub relies on CosmWasm for permissioned smart contract capabilities, making its continued maintenance essential to the Hub’s operations and future upgrades. Many consumer chains utilizing the Hub’s Interchain Security have built their infrastructure on CosmWasm, making the framework vital to sustaining and expanding the ecosystem. By committing to early funding, the Cosmos Hub secures priority consideration for its technical needs and those of its consumer chains in Confio’s development roadmap. By taking the first step in committing funding, AADAO and Neutron hope that their initiative will encourage additional contributions from other ecosystem stakeholders, ensuring the financial stability of CosmWasm and the broader interchain ecosystem. About Atom Accelerator DAO (AADAO) Atom Accelerator DAO (AADAO) is a governance-mandated DAO of the Cosmos Hub. They have been formed and mandated to drive value for the Cosmos Hub & ATOM, supporting initiatives that either directly create economic impact or produce value that can drive its growth. From this, their current primary mandates are public grants & venture grants. Atom Accelerator’s venture arm strategically invests in promising early-stage web3 founders and startups both within and outside of the Cosmos Hub’s ecosystem. As experts within the Cosmos ecosystem, Atom Accelerator offers expertise, guidance, and support while driving value toward ATOM. Website Website Website Contact Marketing & Comms Lead Mr. T Atom Accelerator DAO press@atomaccelerator.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Atom Accelerator Puts Weight Behind CosmWasm Coalition, Offers Matching Funding With Neutron

Atom Accelerator stavlja težinu iza koalicije CosmWasm, nudi finansiranje u skladu sa Neutronom

Atom Accelerator lægger vægt bag CosmWasm Coalition, tilbyder matchende finansiering med neutron

Ο επιταχυντής Atom βάζει βάρος πίσω από το CosmWasm Coalition, προσφέρει αντίστοιχη χρηματοδότηση με νετρόνιο

Atom Accelerator respalda la coalición CosmWasm y ofrece financiación equivalente a la de Neutron

Atom Azeleragailuak pisua jartzen du CosmWasm koalizioaren atzean, eta Neutroiarekin pareko finantzaketa eskaintzen du

L'accélérateur Atom soutient la coalition CosmWasm et propose un financement de contrepartie avec Neutron

O Atom Accelerator pon peso detrás da Coalición CosmWasm e ofrece financiamento equivalente con Neutron

מאיץ האטום שם משקל מאחורי קואליציית CosmWasm, מציע מימון תואם עם ניוטרון

Atom AcceleratorがCosmWasm連合を支援し、Neutronにマッチング資金を提供

Atom Accelerator coloca peso por trás da Coalizão CosmWasm e oferece financiamento correspondente com Neutron

அணு முடுக்கி CosmWasm கூட்டணிக்கு பின்னால் எடை போடுகிறது, நியூட்ரானுடன் பொருந்தக்கூடிய நிதியை வழங்குகிறது

Inilalagay ng Atom Accelerator ang Timbang sa Likod ng CosmWasm Coalition, Nag-aalok ng Pagtutugma ng Pagpopondo Sa Neutron

DUBAI, United Arab Emirates, December 10th, 2024/Chainwire/--DWF Labs, the new generation crypto market maker and investor, announced the launch of a $20 million fund dedicated to supporting the development of autonomous AI agents. This initiative underscores DWF Labs’s commitment to unlock the next generation of artificial intelligence and crypto technology. The new fund aims to support Web3 projects building next-generation AI agent solutions that have the potential to transform industries and redefine the digital economy. Alongside financial backing, recipients will receive comprehensive support to accelerate their growth and adoption. Eligible projects can also receive up to $100,000 of cloud server credits to help optimise performance and scale their infrastructure seamlessly. Additionally, these crypto projects can access strategic advisory services and collaboration opportunities with leading blockchain ecosystems.These partnerships are designed to facilitate the integration of AI-driven applications within decentralised networks. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” Eligible projects will be assessed on their potential to drive innovation and create meaningful impact across industries such as finance, logistics, entertainment, and governance. This initiative reflects DWF Labs’s commitment in supporting cutting-edge technologies to drive adoption and innovation within the Web3 ecosystem. By enabling projects that leverage the intersection of artificial intelligence and decentralised systems, this fund aims to unlock the next generation of AI innovation. The $20 million AI Agent Fund is now open for applications. Apply now: https://ww.dwf-labs.com/ai-agent-fund About DWF Labs DWF Labs is the new generation Web3 investor and market maker, one of the world's largest high-frequency cryptocurrency trading entities, which trades spot and derivatives markets on over 60 top exchanges. Contact Head of Communications Lynn Chia pr@dwf-labs.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, United Arab Emirates, December 10th, 2024/Chainwire/--DWF Labs, the new generation crypto market maker and investor, announced the launch of a $20 million fund dedicated to supporting the development of autonomous AI agents. This initiative underscores DWF Labs’s commitment to unlock the next generation of artificial intelligence and crypto technology. The new fund aims to support Web3 projects building next-generation AI agent solutions that have the potential to transform industries and redefine the digital economy. Alongside financial backing, recipients will receive comprehensive support to accelerate their growth and adoption. Eligible projects can also receive up to $100,000 of cloud server credits to help optimise performance and scale their infrastructure seamlessly. Additionally, these crypto projects can access strategic advisory services and collaboration opportunities with leading blockchain ecosystems.These partnerships are designed to facilitate the integration of AI-driven applications within decentralised networks. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” Eligible projects will be assessed on their potential to drive innovation and create meaningful impact across industries such as finance, logistics, entertainment, and governance. This initiative reflects DWF Labs’s commitment in supporting cutting-edge technologies to drive adoption and innovation within the Web3 ecosystem. By enabling projects that leverage the intersection of artificial intelligence and decentralised systems, this fund aims to unlock the next generation of AI innovation. The $20 million AI Agent Fund is now open for applications. Apply now: https://ww.dwf-labs.com/ai-agent-fund https://ww.dwf-labs.com/ai-agent-fund https://ww.dwf-labs.com/ai-agent-fund About DWF Labs DWF Labs is the new generation Web3 investor and market maker, one of the world's largest high-frequency cryptocurrency trading entities, which trades spot and derivatives markets on over 60 top exchanges. Contact Head of Communications Lynn Chia pr@dwf-labs.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

DWF Labs Launches $20 Million AI Agent Fund To Accelerate Innovation In Autonomous AI Technologies

DWF Labs, avtonom süni intellekt texnologiyalarında innovasiyaları sürətləndirmək üçün 20 milyon dollarlıq süni intellekt agenti fondunu işə salır

DWF Labs lancerer $20 millioner AI Agent Fund for at accelerere innovation i autonome AI-teknologier

DWF Labs lanza un fondo de 20 millones de dólares para agentes de IA con el fin de acelerar la innovación en tecnologías de IA autónomas

DWF Labs pokreće 20 milijuna dolara vrijedan AI Agent Fund za ubrzavanje inovacija u autonomnim AI tehnologijama

DWF Labs、自律型AI技術のイノベーションを加速させるため2,000万ドルのAIエージェントファンドを設立

DWF Labs იწყებს 20 მილიონი დოლარის AI აგენტ ფონდს, რათა დააჩქაროს ინოვაციები ავტონომიურ AI ტექნოლოგიებში

DWF Labs, 자율 AI 기술 혁신 가속화를 위해 2,000만 달러 규모의 AI 에이전트 펀드 출시

DWF Labs Ebandi Fonds Agent AI Ya 20 Millions Ya Dollars Pona Ko accélérer Innovation Na ba Technologies Autonomes AI

Laboratwari ya DWF yatangije miliyoni 20 zamadorali y’ikigega cya AI cyo kwihutisha udushya muri tekinoroji ya AI yigenga

DWF Labs Inotangisa $20 Mamirioni AI Agent Fund Kuti Ikurumidze Innovation MuAutonomous AI Technologies

தன்னாட்சி AI தொழில்நுட்பங்களில் புதுமைகளை துரிதப்படுத்த DWF ஆய்வகங்கள் $20 மில்லியன் AI முகவர் நிதியைத் தொடங்குகின்றன

DWF Labs, Otonom Yapay Zeka Teknolojilerinde Yeniliği Hızlandırmak İçin 20 Milyon Dolarlık Yapay Zeka Temsilcisi Fonu Başlattı

Accelerating Diffusion Models with TheStage AI: 
A Case Study of Recraft's 20B and Red Panda models

Aceleración de modelos de difusión con TheStage AI: 
Un estudio de caso de los modelos 20B y Red Panda de Recraft

Accelerazione dei modelli di diffusione con TheStage AI: uno studio di caso dei modelli 20B e Red Panda di Recraft

TheStage AI による拡散モデルの高速化:
Recraft の 20B および Red Panda モデルのケース スタディ

TheStage AI көмегімен диффузиялық модельдерді жеделдету: 
Recraft 20B және Red Panda үлгілерінің жағдайлық зерттеуі

Mempercepatkan Model Resapan dengan TheStage AI: 
Kajian Kes model 20B dan Red Panda Recraft

Versnellen van diffusiemodellen met TheStage AI: 
Een case study van Recraft's 20B en Red Panda modellen

Go akgofiša Mehlala ya Phatlalatšo ka TheStage AI: 
Thuto ya Mohlala ya dikai tša Recraft tša 20B le Red Panda

Ускорение диффузионных моделей с помощью TheStage AI: 
Исследование моделей Recraft 20B и Red Panda

Zrýchlenie modelov difúzie pomocou AI TheStage: 
Prípadová štúdia modelov 20B a Red Panda spoločnosti Recraft

Modelet e Përshpejtimit të Difuzionit me TheStage AI: 
Një rast studimor i modeleve 20B dhe Red Panda të Recraft

ምቅልጣፍ ሞዴላት ዝርገሐ ብ TheStage AI፤ 
መጽናዕቲ ፍጻመ ናይ ሪክራፍት 20Bን ሬድ ፓንዳን ሞዴላት

TheStage AI ile Difüzyon Modellerini Hızlandırma: 
Recraft'ın 20B ve Red Panda modellerinin bir vaka çalışması

Stellar Community Fund Drives Innovation in Blockchain Development The Stellar Community Fund (SCF) is set to launch Cohort 9 of its Kickstart Program, providing developers and founders with an exceptional opportunity to build, launch, and scale their projects on the Stellar network. Since the launch of the Soroban smart contract platform in May, the Kickstart Program is also open to builders working on DeFi projects and other decentralized applications. Applications for the Kickstart program are now open and will close on November 17, 2024, with the program running from December 2-6, 2024. Kickstart Program - 5-Day Virtual Accelerator The SCF Kickstart program is a structured, five-day virtual boot camp that provides early-stage developers and founders with hands-on mentorship and a path to potentially access funding from the Stellar Community Fund. This program is specifically designed for participants who have a concept and want to transform their ideas into tangible projects built on Stellar. Each day of the program focuses on a key development area: December 2, 2024 - Define Your Minimum Viable Product
December 3, 2024 - Create a UI/UX Mockup
December 4, 2024 - Outline Technical Architecture
December 5, 2024 - Develop a Go-to-Market Strategy
December 6, 2024 - Pitch Your Product to Industry Experts After completing the Kickstart boot camp, developers can apply for the SCF Kickstart Award, potentially earning up to USD $15,000* in XLM to validate and develop their projects. Projects that demonstrate initial traction may then become eligible for the SCF Build Award. Build Program - Roadmap To Launch The SCF Build Program supports projects that have moved beyond the concept phase and are ready to scale toward market launch. Offering up to USD $150,000* in XLM, this award assists teams with development costs, community integration, and resources needed to finalize their products. The Build Program benefits include: Financial Support - Up to six months of development costs
Access to Stellar LaunchKit - Includes audit credits, infrastructure offers, and additional funding mechanisms
Community Integration - Stellar actively supports partners and developers on the network To apply, teams must submit a detailed proposal outlining their project goals, budget, and how they meet eligibility criteria. The application process includes community voting and a panel evaluation, with funding distributed in tranches based on development milestones, ensuring projects stay on track. How To Apply Developers interested in joining the Kickstart program can apply at: https://communityfund.stellar.org/kickstart. Following the November 17th, 2024 deadline, selected participants will receive preparation materials and guidance from the Stellar Development Foundation and the SCF team. Teams that complete the Kickstart program and demonstrate project potential will have the option to scale further with the SCF Build Award. The USD valuation of the budget request in XLM will be calculated using the CF Stellar Lumens-Dollar Settlement Price on the day of payment as administered, maintained, and reported by the cryptocurrency index provider CF Benchmarks Ltd. (using the ticker XLMUSD_RR) (available at https://www.cfbenchmarks.com/indices/XLMUSD_RR). Learn more in the SCF Handbook. Stellar Community Fund Drives Innovation in Blockchain Development The Stellar Community Fund (SCF) is set to launch Cohort 9 of its Kickstart Program, providing developers and founders with an exceptional opportunity to build, launch, and scale their projects on the Stellar network. Since the launch of the Soroban smart contract platform in May, the Kickstart Program is also open to builders working on DeFi projects and other decentralized applications. Stellar Community Fund (SCF) Stellar Community Fund (SCF) Soroban smart contract platform Soroban smart contract platform Applications for the Kickstart program are now open and will close on November 17, 2024, with the program running from December 2-6, 2024. Kickstart Program - 5-Day Virtual Accelerator The SCF Kickstart program is a structured, five-day virtual boot camp that provides early-stage developers and founders with hands-on mentorship and a path to potentially access funding from the Stellar Community Fund. This program is specifically designed for participants who have a concept and want to transform their ideas into tangible projects built on Stellar . Stellar Stellar Each day of the program focuses on a key development area: December 2, 2024 - Define Your Minimum Viable Product December 3, 2024 - Create a UI/UX Mockup December 4, 2024 - Outline Technical Architecture December 5, 2024 - Develop a Go-to-Market Strategy December 6, 2024 - Pitch Your Product to Industry Experts December 2, 2024 - Define Your Minimum Viable Product December 3, 2024 - Create a UI/UX Mockup December 4, 2024 - Outline Technical Architecture December 5, 2024 - Develop a Go-to-Market Strategy December 6, 2024 - Pitch Your Product to Industry Experts After completing the Kickstart boot camp, developers can apply for the SCF Kickstart Award, potentially earning up to USD $15,000* in XLM to validate and develop their projects. Projects that demonstrate initial traction may then become eligible for the SCF Build Award. Build Program - Roadmap To Launch The SCF Build Program supports projects that have moved beyond the concept phase and are ready to scale toward market launch. Offering up to USD $150,000* in XLM, this award assists teams with development costs, community integration, and resources needed to finalize their products. SCF Build Program SCF Build Program The Build Program benefits include: Financial Support - Up to six months of development costs Access to Stellar LaunchKit - Includes audit credits, infrastructure offers, and additional funding mechanisms Community Integration - Stellar actively supports partners and developers on the network Financial Support - Up to six months of development costs Access to Stellar LaunchKit - Includes audit credits, infrastructure offers, and additional funding mechanisms Community Integration - Stellar actively supports partners and developers on the network To apply, teams must submit a detailed proposal outlining their project goals, budget, and how they meet eligibility criteria. The application process includes community voting and a panel evaluation, with funding distributed in tranches based on development milestones, ensuring projects stay on track. How To Apply Developers interested in joining the Kickstart program can apply at: https://communityfund.stellar.org/kickstart. https://communityfund.stellar.org/kickstart . https://communityfund.stellar.org/kickstart Following the November 17th, 2024 deadline, selected participants will receive preparation materials and guidance from the Stellar Development Foundation and the SCF team. Teams that complete the Kickstart program and demonstrate project potential will have the option to scale further with the SCF Build Award. Stellar Development Foundation Stellar Development Foundation The USD valuation of the budget request in XLM will be calculated using the CF Stellar Lumens-Dollar Settlement Price on the day of payment as administered, maintained, and reported by the cryptocurrency index provider CF Benchmarks Ltd. (using the ticker XLMUSD_RR) (available at https://www.cfbenchmarks.com/indices/XLMUSD_RR). Learn more in the SCF Handbook. The USD valuation of the budget request in XLM will be calculated using the CF Stellar Lumens-Dollar Settlement Price on the day of payment as administered, maintained, and reported by the cryptocurrency index provider CF Benchmarks Ltd. (using the ticker XLMUSD_RR) (available at https://www.cfbenchmarks.com/indices/XLMUSD_RR ). Learn more in the SCF Handbook . https://www.cfbenchmarks.com/indices/XLMUSD_RR https://www.cfbenchmarks.com/indices/XLMUSD_RR SCF Handbook SCF Handbook

Can The Stellar Accelerator Kickstart Your Web3 Idea?

Може ли Stellar Accelerator да стартира вашата Web3 идея?

¿Puede el acelerador Stellar impulsar su idea Web3?

L’accélérateur Stellar peut-il donner un coup de fouet à votre idée Web3 ?

Կարո՞ղ է Stellar Accelerator-ը սկսել ձեր Web3 գաղափարը:

Stellar Accelerator は Web3 のアイデアを始動させることができますか?

Na The Stellar Accelerator e ka Kickstart Kgopolo ya Gago ya Web3?

Czy akcelerator Stellar może dać początek Twojemu pomysłowi na Web3?

Dardariyaha Stellar ma bilaabi karaa fikradaada Web3?

Може ли Стеллар Аццелератор покренути вашу Веб3 идеју?

“Stellar Accelerator” Web3 ideýaňyzy başlap bilermi?

Stellar Accelerator Web3 Fikrinizi Harekete Geçirebilir mi?

Ngaba i-Stellar Accelerator iyakwazi ukuqala imbono yakho yeWeb3?

Primer on Large Language Model (LLM) Inference Optimizations: 2. Introduction to Artificial Intelligence (AI) Accelerators

Primer on Large Language Model (LLM) Inference Optimizations: 2. Úvod do akcelerátorů umělé inteligence (AI)

Introducción a las optimizaciones de inferencia de modelos de lenguaje grandes (LLM): 2. Introducción a los aceleradores de inteligencia artificial (IA)

Manual sobre as optimizacións de inferencia do modelo de linguaxe grande (LLM): 2. Introdución aos aceleradores de intelixencia artificial (IA)

वृहद भाषा मॉडल (एलएलएम) अनुमान अनुकूलन पर प्राइमर: 2. कृत्रिम बुद्धिमत्ता (एआई) त्वरक का परिचय

Introduzione alle ottimizzazioni dell'inferenza del Large Language Model (LLM): 2. Introduzione agli acceleratori dell'intelligenza artificiale (IA)

大規模言語モデル (LLM) 推論最適化入門: 2. 人工知能 (AI) アクセラレータの概要

Primer on Large Language Model (LLM) დასკვნის ოპტიმიზაცია: 2. შესავალი ხელოვნური ინტელექტის (AI) ამაჩქარებლებში

Үлкен тілдік үлгідегі (LLM) қорытындыны оңтайландырудағы праймер: 2. Жасанды интеллект (AI) үдеткіштеріне кіріспе

Том хэлний загвар (LLM) дүгнэлтийг оновчтой болгох праймер: 2. Хиймэл оюун ухаан (AI) хурдасгуурын танилцуулга

Horudhac ku saabsan Qaabka Luqadda Weyn (LLM) Kordhinta Farshaxanka: 2. Horudhaca Sirdoonka Artificial (AI) Dardariyeyaasha

Primer on Large Language Model (LLM) Inference Optimizations: 2. Panimula sa Artificial Intelligence (AI) Accelerators

Sách nhập môn về Tối ưu hóa suy luận mô hình ngôn ngữ lớn (LLM): 2. Giới thiệu về bộ tăng tốc trí tuệ nhân tạo (AI)

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Recasting Three-Peak Exclusion Limits on B − L Dark Matter via Accelerometer Studies

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. This paper is available on arxiv under CC BY 4.0 DEED license. [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith.

Enhanced Vector Dark Matter Detection in Accelerometer Studies Through a Three-Peak Analysis

ZUG, Switzerland, October 13th, 2024/Chainwire/--Powerledger(POWR) has officially completed its integration with the Solana ecosystem, accelerating the pace of innovation in the global sustainability markets. This move combines Solana’s cutting-edge blockchain technology with Powerledger’s proven energy and environmental commodities trading and energy tracking solutions, setting the stage for faster, more efficient, and cost-effective clean energy solutions worldwide. On October 1, 2024, Powerledger began the deprecation of its own blockchain, marking a transition for the POWR token across both Ethereum and Solana. This dual-chain approach unlocks potential for the tokenisation, trading, and tracking of renewable energy assets, including excess clean energy, renewable energy certificates (RECs) and carbon credits (CCs), while driving global environmental accountability. Powerledger’s proprietary energy solutions are now transitioned to Solana mainnet. "With our new Solana POWR token, we're excited to leverage Solana's network, this allows for lower fees and faster processing, aligning with our vision to make clean energy more efficient and accessible for all,” said John Bulich, Co-founder & Director, Powerledger. Solana POWR: Speed, Efficiency, and Sustainability In Action. This integration enables Powerledger’s platform to scale faster, support high-volume energy and environmental commodities transactions, and contribute to a more efficient and decentralised energy future for global sustainability efforts. This integration with Solana mainnet offers: POWR token availability: The POWR token is now available on both Ethereum and Solana, with no changes to the total token supply. Token swap mechanism ensures that for every POWR token minted on Solana, an equivalent amount is locked on Ethereum, preserving the integrity of the tokenomics and preventing inflation.
Enhanced flexibility and interoperability: The dual-chain approach ensures that POWR remains accessible to users who prefer Ethereum, while also leveraging Solana’s powerful infrastructure and vibrant community to drive new sustainability solutions and collaboration.
POWR as a payment token: POWR will continue to serve as a payment token for platform services across both Ethereum and Solana, incentivising green behaviours such as offsetting carbon emissions and reducing energy consumption. Powerledger also completed the integration of its own energy platform with Solana, leveraging Solana’s latest tools and technology. https://www.youtube.com/embed/DR-AQIyk9V0 Powerledger (POWR) is set to play a pivotal role in enabling the tokenisation and trading of renewable energy assets, helping businesses meet their sustainability goals while making energy markets more transparent and accessible for all. About Powerledger Powerledger is a Web3 company that creates pioneering solutions that solve pressing energy challenges, enabling access to cheaper and cleaner electricity and transparent environmental trading marketplace. Founded in 2016, Powerledger is known for being Australia’s first and most successful ICO. Powerledger has previously experimented with Bitcoin and Ethereum forks before transitioning to a hard fork on Solana last year. Now, headquartered in Zug, Powerledger is recognised as one of the top 50 companies in Crypto Valley, Switzerland. For more information, please visit https://www.powerledger.io/ Contact Snehal Pawar Powerledger pr@powerledger.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. ZUG, Switzerland, October 13th, 2024/Chainwire/--Powerledger(POWR) has officially completed its integration with the Solana ecosystem, accelerating the pace of innovation in the global sustainability markets. This move combines Solana’s cutting-edge blockchain technology with Powerledger’s proven energy and environmental commodities trading and energy tracking solutions, setting the stage for faster, more efficient, and cost-effective clean energy solutions worldwide. On October 1, 2024, Powerledger began the deprecation of its own blockchain, marking a transition for the POWR token across both Ethereum and Solana. This dual-chain approach unlocks potential for the tokenisation, trading, and tracking of renewable energy assets, including excess clean energy, renewable energy certificates (RECs) and carbon credits (CCs), while driving global environmental accountability. Powerledger’s proprietary energy solutions are now transitioned to Solana mainnet. "With our new Solana POWR token, we're excited to leverage Solana's network, this allows for lower fees and faster processing, aligning with our vision to make clean energy more efficient and accessible for all,” said John Bulich, Co-founder & Director, Powerledger. "With our new Solana POWR token, we're excited to leverage Solana's network, this allows for lower fees and faster processing, aligning with our vision to make clean energy more efficient and accessible for all,” said John Bulich, Co-founder & Director, Powerledger. Solana POWR: Speed, Efficiency, and Sustainability In Action. This integration enables Powerledger’s platform to scale faster, support high-volume energy and environmental commodities transactions, and contribute to a more efficient and decentralised energy future for global sustainability efforts. This integration with Solana mainnet offers: POWR token availability: The POWR token is now available on both Ethereum and Solana, with no changes to the total token supply. Token swap mechanism ensures that for every POWR token minted on Solana, an equivalent amount is locked on Ethereum, preserving the integrity of the tokenomics and preventing inflation. Enhanced flexibility and interoperability: The dual-chain approach ensures that POWR remains accessible to users who prefer Ethereum, while also leveraging Solana’s powerful infrastructure and vibrant community to drive new sustainability solutions and collaboration. POWR as a payment token: POWR will continue to serve as a payment token for platform services across both Ethereum and Solana, incentivising green behaviours such as offsetting carbon emissions and reducing energy consumption. Powerledger also completed the integration of its own energy platform with Solana, leveraging Solana’s latest tools and technology. POWR token availability: The POWR token is now available on both Ethereum and Solana, with no changes to the total token supply. Token swap mechanism ensures that for every POWR token minted on Solana, an equivalent amount is locked on Ethereum, preserving the integrity of the tokenomics and preventing inflation. Enhanced flexibility and interoperability: The dual-chain approach ensures that POWR remains accessible to users who prefer Ethereum, while also leveraging Solana’s powerful infrastructure and vibrant community to drive new sustainability solutions and collaboration. POWR as a payment token: POWR will continue to serve as a payment token for platform services across both Ethereum and Solana, incentivising green behaviours such as offsetting carbon emissions and reducing energy consumption. Powerledger also completed the integration of its own energy platform with Solana, leveraging Solana’s latest tools and technology. https://www.youtube.com/embed/DR-AQIyk9V0 Powerledger (POWR) is set to play a pivotal role in enabling the tokenisation and trading of renewable energy assets, helping businesses meet their sustainability goals while making energy markets more transparent and accessible for all. About Powerledger Powerledger is a Web3 company that creates pioneering solutions that solve pressing energy challenges, enabling access to cheaper and cleaner electricity and transparent environmental trading marketplace. Founded in 2016, Powerledger is known for being Australia’s first and most successful ICO. Powerledger Powerledger Powerledger has previously experimented with Bitcoin and Ethereum forks before transitioning to a hard fork on Solana last year. Now, headquartered in Zug, Powerledger is recognised as one of the top 50 companies in Crypto Valley, Switzerland. For more information, please visit https://www.powerledger.io/ https://www.powerledger.io/ https://www.powerledger.io/ Contact Snehal Pawar Powerledger pr@powerledger.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Powerledger Completes Integration With Solana, Accelerating The Pace Of Innovation In Sustainability

Powerledger завяршае інтэграцыю з Solana, паскараючы тэмпы інавацый у галіне ўстойлівага развіцця

Powerledger завършва интеграцията със Solana, ускорявайки темпото на иновации в устойчивостта

Powerledger dovršava integraciju sa Solanom, ubrzavajući tempo inovacija u održivosti

Powerledger completa la integración con Solana y acelera el ritmo de la innovación en sostenibilidad

Powerledger täydentää integraation Solanan kanssa, mikä nopeuttaa kestävän kehityksen innovaatiotahtia

PowerledgerがSolanaとの統合を完了し、持続可能性におけるイノベーションのペースを加速

Powerledger បញ្ចប់ការរួមបញ្ចូលជាមួយ Solana ដោយបង្កើនល្បឿននៃការច្នៃប្រឌិតប្រកបដោយនិរន្តរភាព

Powerledger Solanawan Huñunakuyta Tukun, Musuqyachiypa Ritmonta Utqaylla Sustentabilidad nisqapi

Powerledger завершает интеграцию с Solana, ускоряя темпы инноваций в области устойчивого развития

Powerledger Inopedzisa Kubatanidzwa naSolana, Inowedzera Iyo Yekukurumidza Yekuvandudza Mukusimudzira.

Поверледгер завршава интеграцију са Соланом, убрзавајући темпо иновација у одрживости

Kinumpleto ng Powerledger ang Pagsasama Sa Solana, Pinapabilis ang Tulin ng Innovation Sa Sustainability

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