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| viXra.org > Data Structures and Algorithms > 2601 |
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[3] viXra:2601.0124 [pdf] submitted on 2026-01-27 00:32:41
Authors: Michael Leventhal
Comments: 306 Pages. 118 figures
This work presents Automata Processing as an algorithmic paradigm in which data-processing problems are formulated as networks of non-deterministic finite automata (NFA) expressed in the Automata Network Markup Language (ANML) and its graphical counterpart ANML-G. The primary aim is to show how moving beyond conventional regular-expression usage enables automata networks—implemented as pattern-matching state-transition elements augmented with counters and related primitives—to serve as a flexible basis for solving a broad range of data-processing tasks, and to provide practical guidance for constructing such machines. The notes synthesize execution semantics and modeling techniques into a tutorial-style reference and a cookbook of worked machines, enabling readers to experiment with design patterns and compose larger solutions from reusable automata building blocks. A semiconductor implementation of a chip able to run ANML descriptions was unveiled by the Micron Corporation in 2013, providing a real-world demonstration of the practicality of the automata processing paradigm. Observations and guidance are given on the use of this semiconductor architecture for Automata Processing.
Category: Data Structures and Algorithms
[2] viXra:2601.0116 [pdf] submitted on 2026-01-25 21:09:30
Authors: Sambuddha Majumder, Jayanta Majumder, Partha P. Chakrabarti
Comments: 22 Pages.
Greggle is a small query language and tool for performing regular path queries over labelled directed graphs. Gruggle is a companion Node.js utility for ingesting, merging, inspecting, and lightly manipulating graphs in the Graphviz dot format. Graphviz is a widely used system for graph visualization; its dot language is simple to author and makes it easy to view results with standard Graphviz tools. Together the two utilities provide a frictionless workflow: Gruggle builds, merges, filters, and styles graphs; Greggle answers expressive path queries with edge—level predicates; and Gruggle can consume Greggle’s annotations (e.g., find-path) to visualize witnesses. This document presents both tools, why they are complementary, and how they can be used jointly in analysis and visualization tasks.
Category: Data Structures and Algorithms
[1] viXra:2601.0013 [pdf] submitted on 2026-01-04 00:50:35
Authors: Ludovic Ngabang
Comments: 2 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)
The integration of Large Language Models (LLMs) into the software development lifecycle represents a shift from constructive programming to curated programming. While current metrics focus on productivity gains and syntactical correctness, this paper argues that these metrics are insufficient to capture the long-term systemic risks introduced by AI.We propose the concept of Epistemic Debt: the divergence between the complexity of a software system and the developer’s cognitive model of that system. Unlike traditional Technical Debt, which is often a conscious trade-off, Epistemic Debt is an invisible accumulation of "unearned" code that functions correctly but lacks ahuman owner who understands its causality. This paper provides a theoretical framework for this phenomenon, classifies the specific rchitectural erosions caused by stochastic code generation, and proposes a "Cognitive Ratchet" methodology to mitigate the collapse of maintainability.
Category: Data Structures and Algorithms
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