Deep learning image processing models have had remarkable success in recent years in generating high quality images. Particularly, the Improved Denoising Diffusion Probabilistic Models (DDPM) have shown superiority in image quality to the state-of-the-art generative models, which motivated us to investigate their capability in the generation of the synthetic electrocardiogram (ECG) signals. In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared. To this end, we devise a pipeline to utilize DDPM in its original 2D form. First, the 1 D ECG time series data are embedded into the 2 D space, for which we employed the Gramian Angular Summation/Difference Fields (GASF/GADF) as well as Markov Transition Fields (MTF) to generate three 2 D matrices from each ECG time series, which when put together, form a 3-channel 2 D datum. Then 2 D DDPM is used to generate 2 D 3-channel synthetic ECG images. The 1 D ECG signals are created by de-embedding the 2 D generated image files back into the 1 D space. This work focuses on unconditional models and the generation of Normal Sinus Beat ECG signals exclusively, where the Normal Sinus Beat class from the MIT-BIH Arrhythmia dataset is used in the training phase. The quality , distribution , and the authenticity of the generated ECG signals by each model are quantitatively evaluated and compared. Our results show that in the proposed pipeline and in the particular setting of this paper, the WGAN-GP model is consistently superior to DDPM in all the considered metrics.

As AI-generated code promises to become an increasingly relied upon tool for software developers, there is a temptation to call for significant changes to early computer science curricula. A move from syntax-focused topics in CS1 toward abstraction and high-level application design seems motivated by the new large language models (LLMs) recently made available. In this position paper however, we advocate for an approach more informed by the AI itself - teaching early CS learners not only how to use the tools but also how to better understand them. Novice programmers leveraging AI-code-generation without proper understanding of syntax or logic can create "black box" code with significant security vulnerabilities. We outline methods for integrating basic AI knowledge and traditional software verification steps into CS1 along with LLMs, which will better prepare students for software development in professional settings.

Fast and accurate eye tracking is a critical task for a range of research in virtual and augmented reality, attention tracking, mobile applications, and medical analysis. While deep neural network models excel at image analysis tasks, existing approaches to segmentation often consider only one class, emphasize classification over segmentation, or come with prohibitively high resource costs. In this work, we propose MinENet, a minimized efficient neural network architecture designed for fast multi-class semantic segmentation. We demonstrate performance of MinENet on the OpenEDS Semantic Segmentation Challenge dataset, against a baseline model as well as standard state-of-the-art neural network architectures - a convolutional neural network (CNN) and a dilated CNN. Our encoder-decoder architecture improves accuracy of multi-class segmentation of eye features in this large-scale high-resolution dataset, while also providing a design that is demonstrably lightweight and efficient.