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"The influence of regional wind patterns on air quality during forest f" by Michael A. Storey, Owen F. Price et al.

Particulate pollution from forest fire smoke threatens the health of communities by increasing the occurrence of respiratory illnesses. Wind drives both fire behaviour and smoke dispersal. Understanding regional wind patterns would assist in effectively managing smoke risk. Sydney, Australia is prone to smoke pollution because it has a large population close to fire-prone eucalypt forests. Here we use the self-organising maps (SOM) technique to identify sixteen unique wind classes for the Sydney region from days with active fires, including identifying sea breeze occurrence. We explored differences in PM2.5 levels between classes and between hazard reduction burning (HRB) and wildfire days. For HRB days, classes with the highest PM2.5 mostly had a sea breeze, whereas better air quality days usually had winds aligned across the region (e.g. all westerly). The wind class with the most HRB days had low wind speeds and a sea breeze and was among the worst wind classes for air quality. For ....

New South Wales , New South , Hazard Reduction Burn , Prescribed Burn , Self Organising Maps ,

"Embedding the Self-Organisation of Deep Feature Maps in the Hamburger " by Jack Humphreys, Markus Hagenbuchner et al.

The popularity of neural networks that explicitly utilise the global correlation structure of their features have become vastly more popular ever since the Transformer architecture was developed. We propose to embed unsupervised Self-Organising Maps within neural networks as a means to model the global correlation structure. By enforcing topological preservation therein, such a neural network is able to represent more complex correlation structures and to produce interpretable visualisations as a byproduct. We validate this approach by comparing with the existing state-of-the-art attention substitute within its own 'Hamburger' framework and by illustrating maps learnt by the module. Overall, this paper serves as a proof of concept for integrating Self-Organising Maps within a supervised network. ....

Self Organising Maps , Deep Learning , Neural Networks , Elf Organizing Feature Maps , Unsupervised Learning , Unsupervised Learning ,

Aeolus and SUNY collaborate on hurricane prediction

Aeolus and SUNY collaborate on hurricane prediction
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Robert Fovell , Environmental Sciences , University Of New York At Albany Department , Aeolus Capital Management , State University , New York , Albany Department , Self Organising Maps ,

Lancaster University: Prestigious award for Lancaster computing Professor

Lancaster University: Prestigious award for Lancaster computing Professor
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Teuvo Kohonen , Vladimir Vapnik , Jose Principe , Nigel Davies , Electronics Engineers , Lancaster University Professor Plamen Angelov , Head Of Lancaster University School Computing , School Of Computing , International Joint Conference On Neural Networks , Institute Of Electircal , International Neural Network Society , Lancaster University , Professor Plamen Angelov , Gabor Award , International Joint Conference , Neural Networks , Professor Angelov , Self Organising Maps , Support Vector Machines ,