A couple of years ago (in April 2017) I completed my master's degree, focusing on the detection of heart disease in electro- and magneto- cardiogram scans. As far as I can tell, the results were state of the art at the time. However, I never posted it on here, and after seeing another paper exploring CNNs for ECGs I thought it might be nice to get some discussion on it. In summary I used a CNN to diagnose myocardial infarction in patients, given their ECG scans. I also applied similar techniques to MCGs generated via a novel non-invasive MCG device.
We propose a new generic type of stochastic neurons, called $q$-neurons, that considers activation functions based on Jackson's $q$-derivatives with stochastic parameters $q$. Our generalization of neural network architectures with $q$-neurons is shown to be both scalable and very easy to implement. We demonstrate experimentally consistently improved performances over state-of-the-art standard activation functions, both on training and testing loss functions.
Greater performance accuracy is implicit in the evolution of robot intelligence, says Fanuc Robotics' Dick Johnson, general manager, material handling. "For example, there is a trend to offer tools that will increase the accuracy of robots by compensating for variations in the manufacturing process. That promises to both decrease robot programming time and also make possible new robot applications."
Schulz, Axel (Technische Universität Darmstadt and SAP Research) | Hadjakos, Aristotelis (Technische Universität Darmstadt, Germany) | Paulheim, Heiko (University of Mannheim) | Nachtwey, Johannes (SAP Research) | Mühlhäuser, Max (Technische Universität Darmstadt)
Real-time information from microblogs like Twitter is useful for different applications such as market research, opinion mining, and crisis management. For many of those messages, location information is required to derive useful insights. Today, however, only around 1% of all tweets are explicitly geotagged. We propose the first multi-indicator method for determining (1) the location where a tweet was created as well as (2) the location of the user's residence. Our method is based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile. An evaluation shows that our method is capable of locating 92% of all tweets with a median accuracy of below 30km, as well as predicting the user's residence with a median accuracy of below 5.1km. With that level of accuracy, our approach significantly outperforms existing work.