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Silence is Golden: Eschewing medicine and the Marine Corps, James Ross finds a contemplative path to fulfillment
Get PDFThere are many translators but not many businesspeople in Buddhism, because it\u27s not often seen as a compatible path with a contemplative lifestyle. -James Ross \u271
The Next Door Opens: Ernesto Esquivel-Amores \u2719 thrives after taking a rollercoaster path to Mayflower Hill
Get PDFAs weird as it sounds, I actually enjoy having those heartbreaking moments when you think, \u27oh, dear God, I think I\u27m gonna die,\u27 he said. They really build character and tell you what you\u27re made of
Karena McKinney: Mapping a Clearer Picture of Air Pollution\u27s Effects
Get PDFIf we want to control air quality and address climate change, the solution won\u27t involve fiddling with nature\u27s emissions-it will be about cleaning up man-made ones. -Karena McKinney, associate professor of atmospheric chemistr
Data-Driven Automatic Dance Improvisation in 2D
No full textDance improvisation, i.e. spontaneously generating and performing dance movements as the music plays, without prior choreography, is a challenging task due to its complexity and ambiguity. Previous works have shown inspiring results in tackling this problem with the combination of convolutional auto-encoders and generative autoregression. However, these works can only take on one dance genre at a time, which requires their training dataset to be hand-labeled. No work so far has exhibited meaningful results in synthesizing multiple dance genres with one single network system. In this paper, we propose an enhanced neural network architecture that features probabilistic motion generation. We train our network on our own dataset which contains dance clips of various genres, ranging from American street dance to Japanese Nico-Nico. Experimental results show that with the probabilistic setup, our system is able to reconcile the ambiguities present in the multi-genre dataset