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Geographic Distribution of Scripps National Spelling Bee Spellers Resembles Geographic Distribution of Child Population in US States upon Implementation of the RSVBee “Wildcard” Program

Kannankeril et al. | Aug 17, 2020

Geographic Distribution of Scripps National Spelling Bee Spellers Resembles Geographic Distribution of Child Population in US States upon Implementation of the RSVBee “Wildcard” Program

The Scripps National Spelling Bee (SNSB) is an iconic academic competition for United States (US) schoolchildren, held annually since 1925. However, the sizes and geographic distributions of sponsored regions are uneven. One state may send more than twice as many spellers as another state, despite similar numbers in child population. In 2018, the SNSB introduced a wildcard program known as RSVBee, which allowed students to apply to compete as a national finalist, even if they did not win their regional spelling bee. In this study, the authors tested the hypothesis that the geographic distribution of SNSB national finalists more closely matched the child population of the US after RSVBee was implemented.

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The Effect of Varying Training on Neural Network Weights and Visualizations

Fountain et al. | Dec 04, 2019

The Effect of Varying Training on Neural Network Weights and Visualizations

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

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Artificial Intelligence Networks Towards Learning Without Forgetting

Kreiman et al. | Oct 26, 2018

Artificial Intelligence Networks Towards Learning Without Forgetting

In their paper, Kreiman et al. examined what it takes for an artificial neural network to be able to perform well on a new task without forgetting its previous knowledge. By comparing methods that stop task forgetting, they found that longer training times and maintenance of the most important connections in a particular task while training on a new one helped the neural network maintain its performance on both tasks. The authors hope that this proof-of-principle research will someday contribute to artificial intelligence that better mimics natural human intelligence.

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Ant Colony Optimization Algorithms with Multiple Simulated Colonies Offer Potential Advantages for Solving the Traveling Salesman Problem and, by Extension, Other Optimization Problems

Wildenhain et al. | May 22, 2015

Ant Colony Optimization Algorithms with Multiple Simulated Colonies Offer Potential Advantages for Solving the Traveling Salesman Problem and, by Extension, Other Optimization Problems

Ant colony optimization algorithms simulate ants moving from point to point on a graph and coordinate their actions, similar to ants laying down pheromones to strengthen a path as it is used more frequently. These ACO algorithms can be applied to the classic traveling salesman problem, which aims to determine the lowest-cost path through a given set of points on a graph. In this study, a novel multiple-colony system was developed that uses multiple simulated ant colonies to generate improved solutions to the traveling salesman problem.

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Mathematical modeling of plant community composition for urban greenery plans

Fang et al. | Jul 05, 2023

Mathematical modeling of plant community composition for urban greenery plans
Image credit: CHUTTERSNAP

Here recognizing the importance of urban green space for the health of humans and other organisms, the authors investigated if mathematical modeling can be used to develop an urban greenery management plan with high eco-sustainability by calculating the composition of a plant community. They optimized and tested their model against green fields in a Beijing city park. Although the compositions predicted by their models differed somewhat from the composition of testing fields, they conclude that by using a mathematical model such as this urban green space can be finely designed to be ecologically and economically sustainable.

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Enhancing the quantum efficiency of a silicon solar cell using one dimensional thin film interferometry

Ahuja et al. | May 03, 2024

Enhancing the quantum efficiency of a silicon solar cell using one dimensional thin film interferometry
Image credit: American Public Power Association

Here, recognizing the need to improve the efficiency of the conversion of solar energy to electrical energy, the authors used MATLAB to mathematically simulate a multi-layered thin film with an without an antireflective coating. They found that the use of alternating ZnO-SiO2 multilayers enhanced the transmission of light into the solar cell, increasing its efficiency and reducing the reflectivity of the Si-Air interface.

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Comparison of three large language models as middle school math tutoring assistants

Ramanathan et al. | May 02, 2024

Comparison of three large language models as middle school math tutoring assistants
Image credit: Thirdman

Middle school math forms the basis for advanced mathematical courses leading up to the university level. Large language models (LLMs) have the potential to power next-generation educational technologies, acting as digital tutors to students. The main objective of this study was to determine whether LLMs like ChatGPT, Bard, and Llama 2 can serve as reliable middle school math tutoring assistants on three tutoring tasks: hint generation, comprehensive solution, and exercise creation.

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Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles

Raman et al. | Jan 12, 2024

Comparative analysis of CO<sub>2</sub> emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles
Image credit: Paul Hanaoka

While some believe that ride-hailing services offer reduced CO2 emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO2 emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.

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