Diagnostic Test Generation for Fault Localization in Printed Neuromorphic Circuits Paper Title: Diagnostic Test Generation for Fault Localization in Printed Neuromorphic Circuits Link to Paper: https://past.date-conference.com/proceedings-archive/2026/DATA/1197.pdf Date: 2026 Paper Type: Test Generation, Fault Detection, Fault Localization, Neuromorphic Computing, Neural Networks, Printed Electronics Short Abstract: Printed electronics enable cheap, flexible, and lightweight devices, but their unreliable manufacturing process makes them prone to defects. This paper proposes a diagnostic testing framework for printed neuromorphic circuits that not only detects faults but also localizes them more effectively, achieving significantly higher diagnostic coverage and reducing undetectable subcircuits compared to traditional methods.

DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing Paper Title: DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing Link to Paper: https://arxiv.org/abs/1809.01266 Date: 4. Sep. 2018 Paper Type: Neural networks, Software testing and debugging, Fuzzing, Fault Detection Short Abstract: The paper introduces DeepHunter, a fuzz-testing framework for deep neural networks that generates semantically valid mutated test cases and uses coverage-guided feedback to uncover hidden defects in safety-critical AI systems. Experiments show it achieves high test validity, improves defect detection over prior methods, and is especially effective at finding issues introduced during DNN quantization and platform migration.

Die 13 1/2 Leben des Käpt'n Blaubär I read, or more like listened to, this as an audiobook in German, and the audiobook version adds so much. I think this is maybe the first time where I would say not only is the audiobook version probably superior (I did not read the physical book), but I would actually recommend it over the book itself. The narrator gives every character a different voice, the music is well done, and the overall audio design in general is great.