Churn Prediction Task in MOOC

Churn prediction is a common task for machine learning applications in business. In this paper, this task is adapted for solving problem of low efficiency of massive open online courses (only 5% of all the students finish their course). The approach is presented on course “Methods and algorithms of the graph theory” held on national platform of online education in Russia. This paper includes all the steps to build an intelligent system to predict students who are active during the course, but not likely to finish it. The first part consists of constructing the right sample for prediction, EDA and choosing the most appropriate week of the course to make predictions on. The second part is about choosing the right metric and building models. Also, approach with using ensembles like stacking is proposed to increase the accuracy of predictions. As a result, a general approach to build a churn prediction model for online course is reviewed. This approach can be used for making the process of online education adaptive and intelligent for a separate student

Shear velocity model for the Kyrgyz Tien Shan from joint inversion of receiver function and surface wave data

The Tien Shan is the largest active intracontinental orogenic belt on Earth. To better understand the processes causing mountains to form at great distances from a plate boundary, we analyse passive source seismic data collected on 40 broad band stations of the MANAS project (2005-2007) and 12 stations of the permanent KRNET seismic network to determine variations in crustal thickness and shear wavespeed across the range. We jointly invert P- and S-wave receiver functions with surface wave observations from both earthquakes and ambient noise to reduce the ambiguity inherent in the images obtained from the techniques applied individually. Inclusion of ambient noise data improves constraints on the upper crust by allowing dispersion measurements to be made at shorter periods. Joint inversion can also reduce the ambiguity in interpretation by revealing the extent to which various features in the receiver functions are amplified or eliminated by interference from multiples. The resulting wavespeed model shows a variation in crustal thickness across the range. We find that crustal velocities extend to ∼ 75 km beneath the Kokshaal Range, which we attribute to underthrusting of the Tarim Basin beneath the southern Tien Shan. This result supports the plate model of intracontinental convergence. Crustal thickness elsewhere beneath the range is about 50 km, including beneath the Naryn Valley in the central Tien Shan where previous studies reported a shallow Moho. This difference apparently is the result of wavespeed variations in the upper crust that were not previously taken into account. Finally, a high velocity lid appears in the upper mantle of the Central and Northern part of the Tien Shan, which we interpret as a remnant of material that may have delaminated elsewhere under the range.km, including beneath the Naryn Valley in the central Tien Shan where previous studies reported a shallow Moho. This difference apparently is the result of wavespeed variations in the upper crust that were not previously taken into account. Finally, a high velocity lid appears in the upper mantle of the Central and Northern part of the Tien Shan, which we interpret as a remnant of material that may have delaminated elsewhere under the range.This is the final published version. It's also available from Oxford Journals at http://gji.oxfordjournals.org/content/199/1/480.full

КОРА И МАНТИЯ БАЙКАЛЬСКОЙ РИФТОВОЙ ЗОНЫ ПО ДАННЫМ ПРИЕМНЫХ ФУНКЦИЙ ПРОДОЛЬНЫХ И ПОПЕРЕЧНЫХ ВОЛН

We have obtained P-wave and S-wave receiver functions for 10 broadband seismograph stations in the Baikal rift zone (BRZ) and inverted them for seismic velocity models of the crust and upper mantle. The thinnest crust (30–35 km) is found in the Baikal basin, the thickest in the East Sayan uplift (45–50 km). Intermediate values (40 km) are found in the BRZ at distances around 100 km from Lake Baikal. A high (at least 1.8) Vp/Vs ratio is observed in the middle and lower crust. It exceeds 2.0 at some stations. In our opinion, the highest Vp/Vs ratios are due to fluid-filled porosity with a high pore pressure. The seismic lithosphere – asthenosphere boundary (LAB) is manifested by a shear velocity drop from 4.5 km/s to 4.0–4.2 km/s. Beneath the Baikal basin, the LAB is located at a depth not more than 50 km, and the S velocity drop is maximal (10 %). A similar structure is found outside the basin, underneath a segment of the East Sayan uplift. At other locations in the BRZ, a typical depth of the LAB varies from 80 to 90 km. Having considered changes in the depth of the 410 km seismic discontinuity, we cannot find any evidence of an elevated temperature of a hypothetical thermal plume beneath the BRZ. Для десяти широкополосных сейсмических станций в Байкальской рифтовой зоне получены приемные функции продольных и поперечных волн и выполнено их совместное обращение в скоростные разрезы. Самая тонкая кора (30–35 км) приурочена к Байкальской впадине, самая толстая – к Восточному Саяну (45–50 км). Промежуточные значения (около 40 км) получены в БРЗ на удалениях около 100 км от Байкала. В средней и нижней коре систематически наблюдается высокое (не менее 1.8) отношение скоростей Vp/Vs, которое на нескольких станциях превышает 2.0. Самые высокие значения мы объясняем присутствием флюида с высоким поровым давлением. Сейсмическая граница литосфера – астеносфера проявляется падением скорости поперечных волн с глубиной от 4.5 до 4.0–4.2 км/с. Под Байкальской впадиной эта граница находится на глубинах, не превышающих 50 км, и понижение скорости поперечных волн в астеносфере достигает максимальных значений (около 10 %). За пределами Байкальской впадины сходная структура наблю­дается под частью Восточного Саяна. В остальных случаях характерное значение глубины границы лито­сфера – астеносфера составляет 80–90 км. Повышение температуры в гипотетическом мантийном плюме под БРЗ по изменению глубины 410-километровой сейсмической границы не обнаружено

Constraints on the interpretation of S-to-P receiver functions

We present results from forward modelling to study the feasibility of using S-to-P converted phases to image the seismic discontinuity structure of the crust and upper mantle. We show that a significant level of P-wave energy arriving before the direct S-wave arrival can interfere with the S-to-P converted phases of interest and may result in Sp receiver function phases that do not represent true earth structure. The source of this P-wave energy is attributable to a number of phases, including those that have undergone multiple reflections off the Earth's surface. For deep focus earthquakes (300–600 km deep), a significant amount of P-wave energy is observed from pPPP, pPPPP and sPPPP phases, and arrives within the same time window as predicted for S-to-P converted phases from the direct S phase arrival. Furthermore, for earthquakes at all depths, interfering P-wave energy arrives within the same time window as predicted for S-to-P converted phases from the SKS phase arrival, limiting the usefulness of SKSp receiver functions for upper mantle imaging. To isolate true Sp receiver function phases from contamination due to other P-wave phases, we find it necessary to stack receiver functions from a range of epicentral distances and depths in order to aid the suppression of noise and other unwanted phases. We provide constraints on the noise levels to be expected as a function of epicentral distance and earthquake depth. We find that the lowest noise levels are achievable by restricting epicentral distance to less than 75 degrees and the depth of earthquakes used to less than 300 km

Stochastic Inversion of P-to-S Converted Waves for Mantle Composition and Thermal Structure: Methodology and Application

We present a new methodology for inverting P‐to‐S receiver function (RF) waveforms directly for mantle temperature and composition. This is achieved by interfacing the geophysical inversion with self‐consistent mineral phase equilibria calculations from which rock mineralogy and its elastic properties are predicted as a function of pressure, temperature, and bulk composition. This approach anchors temperatures, composition, seismic properties, and discontinuities that are in mineral physics data, while permitting the simultaneous use of geophysical inverse methods to optimize models of seismic properties to match RF waveforms. Resultant estimates of transition zone (TZ) topography and volumetric seismic velocities are independent of tomographic models usually required for correcting for upper mantle structure. We considered two end‐member compositional models: the equilibrated equilibrium assemblage (EA) and the disequilibrated mechanical mixture (MM) models. Thermal variations were found to influence arrival times of computed RF waveforms, whereas compositional variations affected amplitudes of waves converted at the TZ discontinuities. The robustness of the inversion strategy was tested by performing a set of synthetic inversions in which crustal structure was assumed both fixed and variable. These tests indicate that unaccounted‐for crustal structure strongly affects the retrieval of mantle properties, calling for a two‐step strategy presented herein to simultaneously recover both crustal and mantle parameters. As a proof of concept, the methodology is applied to data from two stations located in the Siberian and East European continental platforms.This work was supported by a grant from the Swiss National Science Foundation (SNF project 200021_159907). B. T. was funded by a Délégation CNRS and Congé pour Recherches et Conversion Thématique from the Université de Lyon to visit the Research School of Earth Sciences (RSES), The Australian National University (ANU). B. T. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 79382

Problems of Method in Planning the Production Infrastructure

The reorientation of the national economy toward the intensive type of reproduction emphasizes the role of such factors as the increasing division of labor and the development of specialization and cooperation in social production. This problem is particularly important for machine building and agriculture. In machine building, this means making the transition from a system of predominantly unspecialized enterprises to a system of narrowly specialized, highly effective enterprises. In agriculture, in addition to this there is the problem of developing interzonal specialization of production and, in particular, of concentrating the production of various crops in the most appropriate zones. In both instances, with the introduction of new forms of production organization, there is a sharp increase in the volume of cooperative deliveries and an increased load on transport, communications, and warehousesâon everything that is commonly called the production infrastructure.