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Machine learning is an increasingly popular method for modeling complex systems. A common machine learning model is the neural network, which can be trained to represent complicated functions to a high accuracy. However, neural networks often grow large and complex. Recent work is looking at how to abstract networks to yield simpler representations while retaining some property of the original network — for instance, such that for every input the abstracted network's output is at least as large as the original. In this work, we build on previous ideas and extend them to also consider the input layer. Sometimes the input vector has a large size while only a few of the elements are significant in the computation of the output. We propose to use a trained neural network model to identify insignificant input elements, i.e., elements which do not contain important information. We show how the presented abstraction method for the input layer can be utilized to achieve this.

To address safety concerns in industrial systems, we propose a framework for forecasting CPU load with respect to a predetermined threshold, allowing customers to add tasks from a predefined library. Existing tools, akin to Windows Task Manager, provide limited insights due to their aggregate nature and high computational overhead. Our approach uses conformal prediction for rapid uncertainty-aware forecasts and Shapley value analysis to quantify individual task contributions to the CPU load. This proof-of-concept framework improves system safety assessment by addressing key research questions in load prediction and validation, paving the way for refined measurement methodologies in industrial applications.

Integrating machine learning into computer architecture simulation offers a new approach to performance analysis, moving away from traditional algorithmic methods. While existing simulators accurately replicate hardware, they often suffer from slow execution, complex documentation, and require deep CPU knowledge, limiting their usability for quick insights. This paper presents a deep learning-based approach for simulating a key CPU component, cache memory. Our model "learns" cache characteristics by observing cache miss distributions, without needing detailed manual modeling. This method accelerates simulations and adapts to different program needs, demonstrating accuracy comparable to traditional simulators. Tested on Sysbench and image processing algorithms, it shows promise for faster, scalable, and hardware-independent simulations.

The validation of Automated Driving Systems (ADSs) has shifted from distance-based metrics to Scenario-Based Testing (SBT). Large Language Models (LLMs) have emerged as powerful tools with potential for generating vehicular scenarios at scale. However, generative models used for direct simulation synthesis produce inadequate output, therefore necessitating a more structured compilation approach. We present HASCO (Hybrid AI Simulation COmpiler), a system that translates natural-language driving scene specifications into executable simulation artifacts (XOSC/XODR files) for the esmini/OpenSCENARIO ecosystem. While LLMs excel at narrative parsing, we demonstrate that direct synthesis of simulation artifacts fails in the vast majority of cases due to hallucinated physics or schema violations. To resolve this, HASCO treats scenario creation as a compilation task rather than a generative one. The pipeline supports three compilation paths: direct synthesis, a Python intermediate (via scenariogeneration), and an ontology-guided path that grounds intent into an intermediate representation before compilation. We further evaluate a self-judging mechanism for automated repair. Across six operating modes evaluated on 40 real-world accident reports, the ontology-guided and Python-based compilers achieve 95% and 90% executability rates, respectively, compared to 5% for direct synthesis. We additionally evaluate outputs on semantic fidelity, positioning HASCO as a robust tool for forensic scene reconstruction.