arXiv subjects

Simon Brown

Publications and source records attributed to Simon Brown.

8 recordsLinked to original sources

arXiv2025

SPARC: Staking Performance And Reward Coopetition

This paper presents a novel staking coopetition design aimed at incentivizing decentralization and continuous growth of economic security within a proof-of-stake system. Staking rewards follow a nonlinear mapping relative to stake size. This affords the highest effective yields to smaller operators, fueling network growth and giving users an incentive to delegate their stake to smaller operators. This prevents the preferential accrual and centralization of stake seen in popular blockchains such as Ethereum, where popular liquid staking protocols control large fractions of the total stake thereby having outsized potential impacts on the economic security of the protocol. The proposed system addresses key challenges such as Sybil attacks and offers a comprehensive framework for future research and implementation. We introduce innovative mechanisms and gamification elements, to enhance user engagement and provide transparency in emissions.

econ.TH↗

arXiv2025

How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios

Dereverberation is an important sub-task of Speech Enhancement (SE) to improve the signal's intelligibility and quality. However, it remains challenging because the reverberation is highly correlated with the signal. Furthermore, the single-channel SE literature has predominantly focused on rooms with short reverb times (typically under 1 second), smaller rooms (under volumes of 1000 cubic meters) and relatively short distances (up to 2 meters). In this paper, we explore real-time low-latency single-channel SE under distant microphone scenarios, such as 5 to 10 meters, and focus on conference rooms and theatres, with larger room dimensions and reverberation times. Such a setup is useful for applications such as lecture demonstrations, drama, and to enhance stage acoustics. First, we show that single-channel SE in such challenging scenarios is feasible. Second, we investigate the relationship between room volume and reverberation time, and demonstrate its importance when randomly simulating room impulse responses. Lastly, we show that for dereverberation with short decay times, preserving early reflections before decaying the transfer function of the room improves overall signal quality.

eess.AS↗

arXiv2024

Exploring Correlation Patterns in the Ethereum Validator Network

There have been several studies into measuring the level of decentralization in Ethereum through applying various indices to indicate the relative dominance of entities in different domains in the ecosystem. However, these indices do not capture any correlation between those different entities, that could potentially make them the subject of external coercion, or covert collusion. We propose an index that measures the relative dominance of entities based on the application of correlation factors. We posit that this approach produces a more nuanced and accurate index of decentralization.

physics.soc-ph↗

arXiv2023

Measuring the Concentration of Control in Contemporary Ethereum

Ethereum is undergoing significant changes to its architecture as it evolves. These changes include its switch to PoS consensus and the introduction of significant infrastructural changes that do not require a change to the core protocol, but that fundamentally affect the way users interact with the network. These changes represent an evolution toward a more modular architecture, in which there exists new exogenous vectors for centralization. This paper builds on previous studies of decentralization of Ethereum to reflect these recent significant changes, and Ethereum's new modular paradigm.

econ.TH↗

arXiv2023

Securing Autonomous Air Traffic Management: Blockchain Networks Driven by Explainable AI

Air Traffic Management data systems today are inefficient and not scalable to enable future unmanned systems. Current data is fragmented, siloed, and not easily accessible. There is data conflict, misuse, and eroding levels of trust in provenance and accuracy. With increased autonomy in aviation, Artificially Intelligent (AI) enabled unmanned traffic management (UTM) will be more reliant on secure data from diverse stakeholders. There is an urgent need to develop a secure network that has trustworthy data chains and works with the requirements generated by UTM. Here, we review existing research in 3 key interconnected areas: (1) blockchain development for secure data transfer between competing aviation stakeholders, (2) self-learning networking architectures that distribute consensus to achieve secure air traffic control, (3) explainable AI to build trust with human stakeholders and backpropagate requirements for blockchain and network optimisation. When connected together, this new digital ecosystem blueprint is tailored for safety critical UTM sectors. We motivate the readers with a case study, where a federated learning UTM uses real air traffic and weather data is secured and explained to human operators. This emerging area still requires significant research and development by the community to ensure it can enable future autonomous air mobility.

cs.NI↗

arXiv2021

Joint Estimation of Extreme Spatially Aggregated Precipitation at Different Scales through Mixture Modelling

Although most models for rainfall extremes focus on point-wise values, it is aggregated precipitation over areas up to river catchment scale that is of the most interest. To capture the joint behaviour of precipitation aggregates evaluated at different spatial scales, parsimonious and effective models must be built with knowledge of the underlying spatial process. Precipitation is driven by a mixture of processes acting at different scales and intensities, e.g., convective and frontal, with extremes of aggregates for typical catchment sizes arising from extremes of only one of these processes, rather than a combination of them. High-intensity convective events cause extreme spatial aggregates at small scales but the contribution of lower-intensity large-scale fronts is likely to increase as the area aggregated increases. Thus, to capture small to large scale spatial aggregates within a single approach requires a model that can accurately capture the extremal properties of both convective and frontal events. Previous extreme value methods have ignored this mixture structure; we propose a spatial extreme value model which is a mixture of two components with different marginal and dependence models that are able to capture the extremal behaviour of convective and frontal rainfall and more faithfully reproduces spatial aggregates for a wide range of scales. Modelling extremes of the frontal component raises new challenges due to it exhibiting strong long-range extremal spatial dependence. Our modelling approach is applied to fine-scale, high-dimensional, gridded precipitation data. We show that accounting for the mixture structure improves the joint inference on extremes of spatial aggregates over regions of different sizes.

stat.AP↗

arXiv2021

Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods

Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other imposing highly restrictive assumptions. To overcome these issues, we propose a model for high-resolution precipitation data, from which we can simulate realistic fields and explore the behaviour of spatial aggregates. Recent developments have seen spatial extensions of the Heffernan and Tawn (2004) model for conditional multivariate extremes, which can handle a wide range of dependence structures. Our contribution is twofold: extensions and improvements of this approach and its model inference for high-dimensional data; and a novel framework for deriving aggregates addressing edge effects and sub-regions without rain. We apply our modelling approach to gridded East-Anglia, UK precipitation data. Return-level curves for spatial aggregates over different regions of various sizes are estimated and shown to fit very well to the data.

stat.ME↗

arXiv2012

Emergence of a Dynamic Super-Structural Order Integrating Antiferroelectric and Antiferrodistortive Competing Instabilities in EuTiO3

Microscopic structural instabilities of EuTiO3 single crystal were investigated by synchrotron x-ray diffraction. Antiferrodistortive (AFD) oxygen octahedral rotational order was observed alongside Ti derived antiferroelectric (AFE) distortions. The competition between the two instabilities is reconciled through a cooperatively modulated structure allowing both to coexist. The electric and magnetic field effect on the modulated AFD order shows that the origin of large magnetoelectric coupling is based upon the dynamic equilibrium between the AFD - antiferromagnetic interactions versus the electric polarization - ferromagnetic interactions.

cond-mat.mtrl-sci↗