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,

CIBC Innovation Banking Provides Growth Capital Financing to Jirav Inc.

Fin Capital, a global asset manager specializing in B2B FinTech, announced today that it was named to Inc. Business Media’s (“Inc.”) annual Founder-Fr

Fin Capital Named to Inc.’s 2023 List of Founder-Friendly Investors

Venture capitalists (VCs) provide investment capital to early-stage startup companies in exchange for a stake in the company. Meet Asymmetric, one of the best young VC firms.

Another Look At Venture Capital: Meet Asymmetric’s Rob Biederman

Investment firm Section 32, led by Google alum Andy Harrison, invests in everything from AI-enabled drug discovery to large language models. Here's a look at the firm's portfolio.

How Former Googlers’ VC Firm Invests In Everything From LLMs To AI Doing Drug Discovery

The growth-at-all-costs startup mindset is falling out of favor with investors — which is not necessarily a bad thing for Minnesota companies

The Venture Capital Correction

There is a staggering $2.5 trillion global trade finance gap that mainly affects SMEs in emerging countries, limiting their ability to access new business opportunities.

BBVA Spark backs Twinco Capital with €50 million debt facility

Twinco Capital, a supply chain fintech focused on sustainable supply chain finance, has secured a €50 million facility with BBVA Spark, a move aimed at helping bridge the $2.5 trillion global trade finance gap,

Twinco Capital, BBVA Spark tackle $2.5 trillion SME trade finance gap

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

Venture Dallas, an annual conference for entrepreneurs and investors to network and listen to seminars, was headlined this year by panels on biotech,

Venture Dallas conference brings innovators and investors together

While the pace of fundraising has slowed down in recent months, the coffers of local venture capital firms are not empty. The Business Journal counted over 15 VC firms active in the tech space ...

These Boston venture capital firms are ready to deploy capital

Accel

accel

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

The TechBeat: 30 Growth Hacking Examples to Accelerate Your Business (10/5/2024)

The TechBeat: 30 Growth Hacking Examples to Accelerate Your Business (10/3/2024)

The TechBeat: 30 Growth Hacking Examples to Accelerate Your Business (10/1/2024)

The TechBeat: 30 Growth Hacking Examples to Accelerate Your Business (9/29/2024)

30 Growth Hacking Examples to Accelerate Your Business

Stories, News & Mentions about ACCEL

Accel’s Stories on HackerNoon

Accel's News & Mentions