The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.
Read More...Uncovering the hidden trafficking trade with geographic data and natural language processing
The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.
Read More...SOS-PVCase: A machine learning optimized lignin peroxidase with polyvinyl chloride (PVC) degrading properties
The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.
Read More...The journey to Proxima Centauri b
Someday, rockets from Earth may be launched towards worlds beyond our solar system. But will these rockets be able to reach their destination within a human lifetime? Ramaswamy and Giovinazzi simulate rocket launches to an Earth-like exoplanet to uncover whether it's physically possible to complete the journey within a lifetime.
Read More...A novel calibration algorithm and its effects on heading measurement accuracy of a low-cost magnetometer
Digital compasses are essential in technology that we use in our everyday lives: phones, vehicles, and more. Li and Liu address the accuracy of these devices by presenting a new algorithm for accurately calibrating low-cost magnetometers.
Read More...Understanding investors behaviors during the COVID-19 outbreak using Twitter sentiment analysis
The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.
Read More...The effect of activation function choice on the performance of convolutional neural networks
With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.
Read More...Effects of different synthetic training data on real test data for semantic segmentation
Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.
Read More...An optimal pacing approach for track distance events
In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.
Read More...The influence of working memory on auditory category learning in the presence of visual stimuli
Here in an effort to better understand how our brains process and remember different categories of information, the authors assessed working memory capacity using an operation span task. They found that individuals with higher working memory capacity had higher overall higher task accuracy regardless of the type of category or the type of visual distractors they had to process. They suggest this may play a role in how some students may be less affected by distracting stimuli compared to others.
Read More...Extending Einstein’s elevator thought experiment to multiple spatial dimensions at the Luxor Hotel & Casino
In this study, the authors conduct a series of experiments within an elevator traveling on an angle to determine if Einstein's Equivalency Principle and motion vector decomposition can be used to calculate the angle of inclination.
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