Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the organic synthesis of increasingly efficient architectures over successive generations. Existing evolutionary synthesis processes, however, have allowed the mating of parent networks independent of architectural alignment, resulting in a mismatch of network structures. We present a preliminary study into the effects of architectural alignment during evolutionary synthesis using a gene tagging system. Surprisingly, the network architectures synthesized using the gene tagging approach resulted in slower decreases in performance accuracy and storage size; however, the resultant networks were comparable in size and performance accuracy to the non-gene tagging networks. Furthermore, we speculate that there is a noticeable decrease in network variability for networks synthesized with gene tagging, indicating that enforcing a like-with-like mating policy potentially restricts the exploration of the search space of possible network architectures.Comment: 5 page

Assessing Architectural Similarity in Populations of Deep Neural Networks

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations. Despite recent research showing the efficacy of multi-parent evolutionary synthesis, little has been done to directly assess architectural similarity between networks during the synthesis process for improved parent network selection. In this work, we present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results show that networks synthesized using architectural alignment (via gene tagging) maintain higher architectural similarities within each generation, potentially restricting the search space of highly efficient network architectures.Comment: 3 pages. arXiv admin note: text overlap with arXiv:1811.0796

The Relationship Between Insufficient Sleep and Mental Health Distress in Ohio Compared to West Virginia and New Jersey

Objective: To compare and establish the importance of the relationship between insufficient sleep and the frequency of mental health distress in Ohio in contrast to that in West Virginia and New Jersey in 2022. Methods: The data used included information on insufficient sleep, frequency of mental health distress, and premature death per each state studied and was collected from County Health Rankings and then analyzed using a Pearson’s correlation, one way analysis of variance, and multiple linear regression. Ohio was chosen as the reference state with New Jersey and West Virginia as comparisons based on their equivalent population s ize and apparent differences in trends in the studied variables. Results: There was a strong and significant positive correlation between insufficient sleep and frequency of mental health distress in all three states in 2022. The percent of insufficient sleep in 2022 within Ohio (40.45%) was betwixt the states under study with New Jersey being lower (38.09%) and West Virginia being higher (43.34%). The percent of frequent mental health distress is highest in West Virginia (21.19%), then Ohio (17.76%), and lowest in New Jersey (13.11%) in 2022. A linear regression revealed that insufficient sleep could explain 87.3% of the variance in the frequency of mental health distress in 2022. When insufficient sleep was controlled for, t he percent of mental distress in Ohio was 1.8% lower than West Virginia and 3.33% higher than New Jersey in this measure in 2022. A linear regression indicated the frequency of mental health distress accounted for 65.5% of the variance in life expectancy in 2022. When controlled for frequency of mental health distress the life expectancy in Ohio was 2.22 years lower than New Jersey and 1.79 years higher than West Virginia in 2022

Noise Suppression and Contrast Enhancement via Bayesian Residual Transform (BRT) in Low-Light Conditions

Very low-light conditions are problematic for current robotic visionalgorithms as captured images are subject to high levels of ISOnoise. We propose a Bayesian Residual Transform (BRT) model forjoint noise suppression and image enhancement for images capturedunder these low-light conditions via a Bayesian-based multiscaleimage decomposition. The BRT models a given image as thesum of residual images, and the denoised image is reconstructedusing a weighted summation of these residual images. We evaluatethe efficacy of the proposed BRT model using the VIP-LowLightdataset, and preliminary results show a notable visual improvementover state-of-the-art denoising methods