High-resolution image analysis using deep Convolutional Neural Networks (CNNs) faces significant memory constraints due to the quadratic growth of intermediate feature maps with input resolution. This paper investigates whether Implicit Neural Representations (INRs), specifically SIRENs, can effectively represent CNN feature maps to reduce memory footprint during training. We address the unique challenge that CNN feature maps are not static signals but evolve continuously as network weights are updated through gradient-based optimization. Through three experiments on a modified All-CNN architecture trained on MNIST, we validate that: (1) SIRENs can fit static feature maps from frozen CNNs with high fidelity (PSNR > 30 dB) regardless of weight initialization; (2) SIRENs can track evolving feature maps during training, though with reduced reconstruction quality compared to static targets; and (3) SIREN-assisted feedforward—where SIRENs predict missing activations in receptive fields—enables classification accuracy (20.97%) above random guessing (10%) but substantially below standard training (95%). While results demonstrate the feasibility of using SIRENs to represent dynamic feature maps, significant challenges remain in maintaining reconstruction fidelity when SIRENs are integrated into the training loop. This proof-of-concept study provides empirical insights into bridging continuous implicit representations with discrete deep learning pipelines and highlights promising directions for future research in memory-efficient high-resolution image analysis.

Configuration management systems are a class of software used to automate system administrative tasks, one of which is the configuration of software systems. Although the automation is less error-prone than manual configuration done by a human, bugs in the source code can still cause configuration errors. This can result into unwanted consequences for the managed software system, some examples are performance degradation, downtime and security breaches.

Bug studies are conducted on previously reported bugs to find out their characteristics. Such findings help with preventing, detecting and fixing bugs, which ultimately causes software to become more reliable. This paper aims to fill in a research gap in the literature surrounding bug studies conducted on configuration management systems. From Salt, a widely used open-source configuration management system, a data-set of 5,896 bugs with fixes was collected. The main research question answered by this paper is ``What are the common patterns can be extracted from bugs found in Salt?'' To answer this, 100 bugs were randomly sampled from this data-set to analyze their symptoms, root causes, impact, fixes, system-dependence and triggers.