Data science or AI wisdom.
Tshitoyan et al. have a paper out that uses NLP, specifically word2vec, to grope towards predictive chemistry. They analyze a corpus of a the abstracts of a few million papers over the previous century with a vocabulary of half a million words. They embed the representation of the words in Read more…
Rominger et al. (doi: 10.1126/sciadv.aat0122 https://advances.sciencemag.org/content/5/6/eaat0122 , open access ) have come up with an astonishingly simple explanation for the distributions of fluctuation in biodiversity. First some definitions. The Phanerozoic is the last half billion years (actually 540 MYr to present.) This work deals with marine invertebrate species during the Read more…
Generative Adversarial Networks have features of legal systems of some countries. There is a decision process (the “law”) that reaches a judgement on conflicting interests. The law evolves to address perceived deficiency, and adversaries devise cleverer ways around it. For example, let’s say you write a program (the law) that Read more…
Where was this when we were grad students absconding from labs to watch March Madness ? Sicilia et al. have a charming paper ( https://arxiv.org/abs/1902.08081 ) on machine learning for basketball. They begin with an annotated database of player and ball positions at 1/25 second resolution for 650 NBA games Read more…
One way to define a privacy measure is through the concept of differential privacy. What is the privacy loss for a person to take part in a survey as opposed to abstaining ? Consider two sets of people differing by one person. What is that person’s privacy loss from being Read more…
An earlier post on Empirical Dynamics and Information Flow, https://clarodatascience.com/2018/06/17/dynamical-systems-information-flow-and-causality/ showed a technique to reconstruct attractors in phase space from measurement of a single coordinate of trajectories orbiting the attractor. Briefly, we construct lagged vectors from one coordinate time series, identify the nearest neighbours for a candidate vector, find their Read more…
Arithmetic is expensive. If one has fewer bits to add and multiply once can do so faster and cheaper. Hence interest in lo-bit neural nets. Zhou et al. have an approach that they test on image recognition that shows one can get away with fewer bits of resolution, for example Read more…
The image below shows correlations between viewing of TV networks at increasing time lags. The central ridge reappears at a lag of a week showing recurrent weekly behaviour. With longer records this would be a natural target for empirical dynamical modelling a la Sugihara, as previously outlined in the post Read more…
A dynamical system is one that evolves with time either stochastically or deterministically. The evolution is described by a set of differential equations. The instantaneous state of a dynamical system is represented as a point in some phase space. As the system evolves the point moves describing a trajectory in Read more…
