You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: including only a smaller number of lower layers, leading to different visual experiences (Fig 3, along the rows). When matching the style representations up to higher layers in the network, local images structures are matched on an increasingly large scale, leading to a smoother and more continuous visual experience. Thus, the visually most appealing images are usually cre- ated by matching the style representation up to the highest layers in the network (Fig 3, last row). Of course, image content and style cannot be completely disentangled. When synthesising an image that combines the content of one image with the style of another, there usually does not exist an image that perfectly matches both constraints at the same time. However, the loss function we minimise during image synthesis contains two terms for content and style respectively, that are well separated (see Methods). We can therefore smoothly regulate the emphasis on either reconstructing the content or the style (Fig 3, along the columns). A strong emphasis on style will result in images that match the appearance of the artwork, effectively giving a texturised version of it, but hardly show any of the photograph’s content (Fig 3, ﬁrst column). When placing strong emphasis on content, one can clearly identify the photograph, but the style of the painting is not as well-matched (Fig 3, last column). For a speciﬁc pair of source images one can adjust the trade-off between content and style to create visually appealing images. Here we present an artiﬁcial neural system that achieves a separation of image content from style, thus allowing to recast the content of one image in the style of any other image. We demonstrate this by creating new, artistic images that combine the style of several well-known paintings with the content of an arbitrarily chosen photograph. In particular, we derive the neural representations for the content and style of an image from the feature responses of high- performing Deep Neural Networks trained on object recognition. To our knowledge this is the ﬁrst demonstration of image features separating content from style in whole natural images. To generate the images that mix the content of a photograph with the style of a painting

Question: How was the ratio α/β of weighting factors for content (α) and style reconstruction(β) used by the authors?
Since it is difficult to satisfy both content and stylistic constraints on the resulting image, the α and β weights in the loss function are used to manipulate the emphases on the content and stylistic representations respectively. Several different ratios of α/β (10^-5, 10^-4, 10^-3, 10^-2) are explored to demonstrate the differences between synthesized images. In general, it allowed smooth and continuous regulation of two separate terms of the loss function, thus producing more visually pleasing images.