Gold Treasure Worth a Fortune Was Hidden in a Forest. For years, Jason Rohrer put out bizarre, beloved video games. Now, with Project Skydrop, he launches the real-world treasure hunt of his dreams. The muddy trail levels out and we stop to catch our breath. Which is good, because hiking with my eyes covered has been a pain in the ass. A voice says: "You can take your blindfold off now." I squint as I get my bearings. Then, after a bit more hiking and some bushwhacking, I finally see it. The thing no one is supposed to know the location of, at least for another few weeks. I have to fight a lizard-brain instinct to reach for it.

This pilot study aims to develop a deep learning model for predicting seismocardiogram (SCG) signals in the dorsoventral direction from the SCG signals in the right-to-left and head-to-foot directions ($\textrm{SCG}_x$ and $\textrm{SCG}_y$). The dataset used for the training and validation of the model was obtained from 15 healthy adult subjects. The SCG signals were recorded using tri-axial accelerometers placed on the chest of each subject. The signals were then segmented using electrocardiogram R waves, and the segments were downsampled, normalized, and centered around zero. The resulting dataset was used to train and validate a long short-term memory (LSTM) network with two layers and a dropout layer to prevent overfitting. The network took as input 100-time steps of $\textrm{SCG}_x$ and $\textrm{SCG}_y$, representing one cardiac cycle, and outputted a vector that mapped to the target variable being predicted. The results showed that the LSTM model had a mean square error of 0.09 between the predicted and actual SCG segments in the dorsoventral direction. The study demonstrates the potential of deep learning models for reconstructing 3-axis SCG signals using the data obtained from dual-axis accelerometers.

When Nathan Kecy graduated from Plymouth State University in New Hampshire a decade ago with a bachelor's degree in communications, he found himself with about $10,000 in debt and few clear career options. He first found work as a door-to-door salesman ("a pyramid scheme," he recalls) and then in telemarketing. Finally he landed a job as an infrastructure specialist for Datamatic, a Texas-based water-meter-technology company. He was traveling across the country installing meters, making a decent salary. But he lost his job after the company restructured in 2012, he said, and soon he found that his skills weren't easily transferable to a new field; Datamatic's technology was proprietary, and his expertise in the company's installation program wasn't appealing to employers outside that particular industry.