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A machine learning approach for abstraction and reasoning problems without large amounts of data

Isik et al. | Jun 25, 2022

A machine learning approach for abstraction and reasoning problems without large amounts of data

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

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A Quantitative Analysis of the Proliferation of Microplastics in Williamston’s Waterways

Schafer et al. | Feb 17, 2019

A Quantitative Analysis of the Proliferation of Microplastics in Williamston’s Waterways

Plastic debris can disrupt marine ecosystems, spread contaminants, and take years to naturally degrade. In this study, Wu et al aim to establish an understanding of the scope of Williamston, Michigan’s microplastics problem, as well as to attempt to find the source of these plastics. Initially, the authors hypothesize that the Williamston Wastewater Treatment Plant was the primary contributor to Williamston’s microplastics pollution. Although they find a general trend of increasing concentrations of microplastics from upstream to downstream, they do not pinpoint the source of Williamston’s microplastics pollution in the present research.

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The Cosmic Microwave Background: Galactic Foregrounds and Faraday Rotation

Connelly et al. | Nov 20, 2017

The Cosmic Microwave Background: Galactic Foregrounds and Faraday Rotation

The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.

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Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Jang et al. | Aug 19, 2016

Peptidomimetics Targeting the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (Plk1) is a master regulator of mitosis, initiating key steps of cell cycle regulation, and its overexpression is associated with certain types of cancer. In this study, the authors carefully designed peptides that were able to bind to Plk1 at a location that is important for its proper localization and function. Future studies could further develop these peptides to selectively target Plk1 in cancer cells and induce mitotic arrest.

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A Simple Printing Solution to Aid Deficit Reduction

Mirchandani et al. | Mar 09, 2014

A Simple Printing Solution to Aid Deficit Reduction

The printing-related expenditure that is budgeted in 2014 for U.S. Federal agencies is $1.8 billion. A sample of five publically available documents produced by various federal agencies is analyzed and the cost savings arising from a change in font type are estimated. The analysis predicts that the Government’s annual savings by switching to Garamond are likely to be about $234 million with worst-case savings of $62 million and best-case savings of $394 million. Indirect benefits arising from a less detrimental impact on the environment due to lower ink production and disposal volumes are not included in these estimates. Times New Roman is not as efficient as Garamond, and the third federally-recommended font, Century Gothic, is actually worse on average than the fonts used in the sample documents.

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The effect of activation function choice on the performance of convolutional neural networks

Wang et al. | Sep 15, 2023

The effect of activation function choice on the performance of convolutional neural networks
Image credit: Tara Winstead

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.

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Cleaning up the world’s oceans with underwater laser imaging

Gurbuz et al. | Jul 07, 2023

Cleaning up the world’s oceans with underwater laser imaging
Image credit: Naja Bertolt Jensen

Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.

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