Zobrazeno 1 - 10
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pro vyhledávání: '"Knottenbelt, A."'
Autor:
Chen, Jing, Elliott, Karen, Knottenbelt, William, van Moorsel, Aad, Orpin, Helen, Robertson, Sheena, Vines, John, Wolter, Katinka
This document presents a research agenda for financial services as a deliverable of UKFin+, a Network Plus grant funded by the Engineering and Physical Sciences Research Council. UKFin+ fosters research collaborations between academic and non-academi
Externí odkaz:
http://arxiv.org/abs/2411.13389
Autor:
Qi, Minfeng, Wang, Qin, Wang, Zhipeng, Zhong, Lin, Zhu, Tianqing, Chen, Shiping, Knottenbelt, William
BRC20 tokens are a type of non-fungible asset on the Bitcoin network. They allow users to embed customized content within Bitcoin satoshis. The related token frenzy has reached a market size of US$2,650b over the past year (2023Q3-2024Q3). However, t
Externí odkaz:
http://arxiv.org/abs/2410.11295
Autor:
Knottenbelt, William, Gao, Zeyu, Wray, Rebecca, Zhang, Woody Zhidong, Liu, Jiashuai, Crispin-Ortuzar, Mireia
Survival analysis is a branch of statistics used for modeling the time until a specific event occurs and is widely used in medicine, engineering, finance, and many other fields. When choosing survival models, there is typically a trade-off between pe
Externí odkaz:
http://arxiv.org/abs/2409.04290
Autor:
Qi, Minfeng, Wang, Qin, Wang, Zhipeng, Schneider, Manvir, Zhu, Tianqing, Chen, Shiping, Knottenbelt, William, Hardjono, Thomas
We present the first Systematization of Knowledge (SoK) on constructing Layer Two (L2) solutions for Bitcoin. We carefully examine a representative subset of ongoing Bitcoin L2 solutions (40 out of 335 extensively investigated cases) and provide a co
Externí odkaz:
http://arxiv.org/abs/2409.02650
The proliferation of blockchain entities (persons or enterprises) exposes them to potential regulatory actions (e.g., being litigated) by regulatory authorities. Regulatory frameworks for crypto assets are actively being developed and refined, increa
Externí odkaz:
http://arxiv.org/abs/2408.11961
Model-based reinforcement learning (MBRL) algorithms learn a dynamics model from collected data and apply it to generate synthetic trajectories to enable faster learning. This is an especially promising paradigm in offline reinforcement learning (RL)
Externí odkaz:
http://arxiv.org/abs/2408.10713
Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key cavea
Externí odkaz:
http://arxiv.org/abs/2404.16399
Technological advancement drives financial innovation, reshaping the traditional finance landscape and redefining user-market interactions. The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology a
Externí odkaz:
http://arxiv.org/abs/2311.17715
In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their
Externí odkaz:
http://arxiv.org/abs/2401.08610
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in big data scen
Externí odkaz:
http://arxiv.org/abs/2310.02554