Gaia16aye was a binary microlensing event discovered in the direction towards the northern Galactic disc and was one of the first microlensing events detected and alerted to by the Gaia space mission. Its light curve exhibited five distinct brightening episodes, reaching up to I=12 mag, and it was covered in great detail with almost 25,000 data points gathered by a network of telescopes. We present the photometric and spectroscopic follow-up covering 500 days of the event evolution. We employed a full Keplerian binary orbit microlensing model combined with the motion of Earth and Gaia around the Sun to reproduce the complex light curve. The photometric data allowed us to solve the microlensing event entirely and to derive the complete and unique set of orbital parameters of the binary lensing system. We also report on the detection of the first-ever microlensing space-parallax between the Earth and Gaia located at L2. The properties of the binary system were derived from microlensing parameters, and we found that the system is composed of two main-sequence stars with masses 0.57±0.05 M and 0.36±0.03 M at 780 pc, with an orbital period of 2.88 years and an eccentricity of 0.30. We also predict the astrometric microlensing signal for this binary lens as it will be seen by Gaia as well as the radial velocity curve for the binary system. Events such as Gaia16aye indicate the potential for the microlensing method of probing the mass function of dark objects, including black holes, in directions other than that of the Galactic bulge. This case also emphasises the importance of long-term time-domain coordinated observations that can be made with a network of heterogeneous telescopes.

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On the basis of the diffusion theory, we suggested a model for forecasting event in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news text clusters (states of the information space). Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angle between the above vector and centroids can be represented as a point wandering on [0,1] segment. This segment contains a trap at the event occurrence threshold point. The wandering point can fall into this trap over time. We have considered probability patterns of transitions between states in the information space. We have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events. We have obtained theoretical time dependence for the probability density function of the parameter distribution of non-stationary time series that describe the information space evolution. The results of simulating the time dependence of the event probability (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate. Experimental verification of the proposed model was carried out using a corpus of texts written in Russian.