arXiv subjects

Josh Brown

Publications and source records attributed to Josh Brown.

3 recordsLinked to original sources

arXiv2026

Decentralized Compute on Untrusted Hardware Using Intel TDX and Encrypted CVMs

The rapid growth of artificial intelligence workloads has generated an unprecedented demand for secure and scalable compute resources. However, centralized cloud providers continue to dominate both pricing and security models. In an increasingly competitive AI landscape, where the compromise of training data or model weights can confer a significant advantage, there is a critical need for a computing infrastructure that safeguards data at rest, in transit, and in use, while remaining affordable and broadly accessible. Furthermore, existing GPU cluster offerings (e.g., 8xH100s, 8xH200s, 8xB200s) create financial barriers that limit access for organizations, startups, and independent researchers seeking secure, high-performance computing environments. This paper introduces a decentralized, confidential computing platform that leverages Intel Trust Domain Extensions (TDX), Intel Trust Authority (ITA) and NVIDIA Confidential Computing (CC) to establish a distributed ecosystem of fully encrypted Confidential Virtual Machines (CVMs). The proposed architecture incentivizes hardware providers to contribute Intel TDX capable compute resources. Each participating provider is provisioned with a freshly instantiated, uniquely encrypted Ubuntu 24.04 CVM, providing data protection across all stages, at rest, in transit, and in use. By decentralizing the confidential computing stack and leveraging confidential computing across independently operated nodes, this work demonstrates a viable alternative to traditional cloud-based infrastructures. The proposed system offers enhanced security assurances, transparent cost structures, and democratized access to enterprise-grade secure compute capabilities, paving the way for a more open, secure, and equitable foundation for next-generation AI development.

cs.CR↗

arXiv2024

Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations

In this research, we examine the capabilities of different mathematical models to accurately predict various levels of the English football pyramid. Existing work has largely focused on top-level play in European leagues; however, our work analyzes teams throughout the entire English Football League system. We modeled team performance using weighted Colley and Massey ranking methods which incorporate player valuations from the widely-used website Transfermarkt to predict game outcomes. Our initial analysis found that lower leagues are more difficult to forecast in general. Yet, after removing dominant outlier teams from the analysis, we found that top leagues were just as difficult to predict as lower leagues. We also extended our findings using data from multiple German and Scottish leagues. Finally, we discuss reasons to doubt attributing Transfermarkt's predictive value to wisdom of the crowd.

stat.AP↗

arXiv2017

$^{137,138,139}$La($n$, $\gamma$) cross sections constrained with statistical decay properties of $^{138,139,140}$La nuclei

The nuclear level densities and $\gamma$-ray strength functions of $^{138,139,140}$La were measured using the $^{139}$La($^{3}$He, $\alpha$), $^{139}$La($^{3}$He, $^{3}$He$^\prime$) and $^{139}$La(d, p) reactions. The particle-$\gamma$ coincidences were recorded with the silicon particle telescope (SiRi) and NaI(Tl) (CACTUS) arrays. In the context of these experimental results, the low-energy enhancement in the A$\sim$140 region is discussed. The $^{137,138,139}$La($n, \gamma)$ cross sections were calculated at $s$- and $p$-process temperatures using the experimentally measured nuclear level densities and $\gamma$-ray strength functions. Good agreement is found between $^{139}$La($n, \gamma)$ calculated cross sections and previous measurements.

nucl-ex↗