Human intelligence is correlated with variation in the protein neuroplastin-65, which is encoded by the NPTN gene. The authors examine the evolution of this gene across different animal species.
Read More...Evolution of Neuroplastin-65
Human intelligence is correlated with variation in the protein neuroplastin-65, which is encoded by the NPTN gene. The authors examine the evolution of this gene across different animal species.
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...A novel deep learning model for visibility correction of environmental factors in autonomous vehicles
Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.
Read More...Automated classification of nebulae using deep learning & machine learning for enhanced discovery
There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.
Read More...Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules
Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.
Read More...Investigating Teen Audism: The Development and Use of a Survey Scale to Measure Misconceptions of the Deaf Community in a Hearing High School
The authors explore hearing students' misconceptions about the Deaf and Hard of Hearing (HoH) community. Results indicate that some misconceptions are more common than others, and that personal experience with individuals in the Deaf and HoH community reduces the frequency of such misconceptions.
Read More...Floating aquatic plants form groups faster through current
Here, the authors sought to investigate the effects of water current on the growth of colonies of duckweed, a floating plant that forms colonies in silent ponds, marshes, lakes , and streams in North America. They found that current flow mediates the formation of colonies, disrupting and recreating the colonies which provides the opportunity for reorganizations that were identified as beneficial.
Read More...Can the Growth Mindset Encourage Girls to Pursue “Male” Careers?
Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.
Read More...Evaluating the effectiveness of machine learning models for detecting AI-generated art
The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.
Read More...Building a video classifier to improve the accuracy of depth-aware frame interpolation
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
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