As a particular type of language, the "static" (neglecting random transcription errors, recombination and mutation) DNA and its transcription pattern over time yields biologically essential s.t. patterns.
e.g. computer languages, are not read and interpreted in one step, but sequentially, thus, their meaningfully arranged vocabulary (e.g. "computer code") can be seen as a s.t. pattern.
Standard signal processing analysis techniques and information theoretical measurements help visualize the historic correlations, but a sound investment strategy should also consider the correlation migration –how the correlation changes (or not) over time. While it is possible to plot the correlation graph for each point in time, we find that Krabec and Venegas method (Krabec, Venegas, 2015) using vector fields allows for mappings with a higher information density, especially for portfolios of a large size –consider that when each Dapp is also an asset, we are facing prohibitory large portfolio sizes. To implement the method we begin by defining the convention for the vector components. From the possible traffic sources for a new project (referral links, social networks, digital advertisement, search engines, email and direct visits) we found that referrals and social are the more prevalent, especially in the early stages of a proposal listing when word of mouth in social networks such as Reddit and the ability of the founding team to generate buzz in media and news sites appears to play a role. The resulting vector field gives rise to a flow. A fluid flow is a very effective way to summarize the dynamics of a portfolio to include an arbitrarily large number of entities, rather than simply scaling up the number and size of correlation graphs (not to mention that for communication purposes, a vector field is also a more intuitive representation of cashflows equivalents). In one hand, the (total) vector magnitudes are a measure of strength, in the other, the interaction between the different assets (as revealed by singularities in the flow) present a portrait of the system- the portfolio attention correlations. In our 32-asset mapping we include 7 Dapps. Figure 13 shows the vector field rendered using 4 techniques to highlight different aspects of the flow.