--This paper addresses the persistent challenge of accurately digitizing paper-based electrocardiogram (ECG) recordings, with a particular focus on robustly handling single leads compromised by signal overlaps--a common yet under-addressed issue in existing methodologies. We propose a two-stage pipeline designed to overcome this limitation. The first stage employs a U-Net based segmentation network, trained on a dataset enriched with overlapping signals and fortified with custom data augmentations, to accurately isolate the primary ECG trace. The subsequent stage converts this refined binary mask into a time-series signal using established digitization techniques, enhanced by an adaptive grid detection module for improved versatility across different ECG formats and scales. Our experimental results demonstrate the efficacy of our approach. The U-Net architecture achieves an Intersection over Union (IoU) of 0.87 for the fine-grained segmentation task. Crucially, our proposed digitization method yields superior performance compared to a well-established baseline technique across both non-overlapping and challenging overlapping ECG samples. For non-overlapping signals, our method achieved a Mean Squared Error (MSE) of 0.0010 and a Pearson Correlation Coefficient ( ρ) of 0.9644, compared to 0.0015 and 0.9366, respectively, for the baseline. On samples with signal overlap, our method achieved an MSE of 0.0029 and a ρ of 0.9641, significantly improving upon the baseline's 0.0178 and 0.8676. This work demonstrates an effective strategy to significantly enhance digitization accuracy, especially in the presence of signal overlaps, thereby laying a strong foundation for the reliable conversion of analog ECG records into analyzable digital data for contemporary research and clinical applications. Electrocardiogram (ECG) serves as a cornerstone in the diagnosis and ongoing monitoring of cardiovascular diseases, which persist as a primary cause of mortality globally [1]. The ability to access and analyze ECG time-series data substantially enhances the efficacy of deep learning-based clinical decision support systems [2].

As a Unity XR prototyper, I'm always eager to explore new technologies and tools that can help me push the boundaries of what's possible. Recently, I have had the pleasure of trying out Auki Lab's ConjureKit SDK and experimenting with its monocular hand tracker (by the way, neither LIDaR nor depth sensors are required, the hand tracker just works with one RGB camera.) In my test scene, I spawned a few spheres, the user can touch the spheres which will deform a bit and move. I needed to create a grid pattern that would make the deformation more visible. Since I'm not an expert in shader programming, I decided to try using ChatGPT (OpenAI) to generate an HLSL script.

Learn how to use the R programming language for data science and machine learning and data visualization! Hello everyone and welcome to the lecture on histograms and this lecture we're going to learn how to create histograms with our. We're going to first start off with installing Gigia plot to will also install a dataset with related the Gilia plot to called a cheesy plot to movies dataset. And before we actually start coding anything I'm going to show you a great cheat sheet resource that our studio provides for you for work and of G-G plot 2. OK. I'm super excited to show you all this. So let's go ahead and jump to our studio. OK so here we are our studio. Let's go ahead and start off by installing the packages we need. You're going to need to install G-G plot 2. So the start off will just go to Head and in the console you can say installed packages in quotes.

In this paper, we present a board game: Square War. The game definition of Square War is similar to the classic Chinese board game Go. Then we propose a mathematical problem of the game Square War. Finally, we show that the problem can be solved by using a method of mixed mathematics and computer science.