The Mathematical Specification of the Statebox Language
June 18, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Statebox Team, Fabrizio Genovese, Jelle Herold
arXiv ID
1906.07629
Category
cs.PL: Programming Languages
Cross-listed
cs.DC,
math.CT
Citations
10
Venue
arXiv.org
Last Checked
3 months ago
Abstract
This document defines the mathematical backbone of the Statebox programming language. In the simplest way possible, Statebox can be seen as a clever way to tie together different theoretical structures to maximize their benefits and limit their downsides. Since consistency and correctness are central requisites for our language, it became clear from the beginning that such tying could not be achieved by just hacking together different pieces of code representing implementations of the structures we wanted to leverage: Rigorous mathematics is employed to ensure both conceptual consistency of the language and reliability of the code itself. The mathematics presented here is what guided the implementation process, and we deemed very useful to release it to the public to help people wanting to audit our work to better understand the code itself.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted