Non-contrastive representation learning for intervals from well logs

The representation learning problem in the oil & gas industry aims to construct a model that provides a representation based on logging data for a well interval. Previous attempts are mainly supervised and focus on similarity task, which estimates closeness between intervals. We desire to build informative representations without using supervised (labelled) data. One of the possible approaches is self-supervised learning (SSL). In contrast to the supervised paradigm, this one requires little or no labels for the data. Nowadays, most SSL approaches are either contrastive or non-contrastive. Contrastive methods make representations of similar (positive) objects closer and distancing different (negative) ones. Due to possible wrong marking of positive and negative pairs, these methods can provide an inferior performance. Non-contrastive methods don't rely on such labelling and are widespread in computer vision. They learn using only pairs of similar objects that are easier to identify in logging data. We are the first to introduce non-contrastive SSL for well-logging data. In particular, we exploit Bootstrap Your Own Latent (BYOL) and Barlow Twins methods that avoid using negative pairs and focus only on matching positive pairs. The crucial part of these methods is an augmentation strategy. Our augmentation strategies and adaption of BYOL and Barlow Twins together allow us to achieve superior quality on clusterization and mostly the best performance on different classification tasks. Our results prove the usefulness of the proposed non-contrastive self-supervised approaches for representation learning and interval similarity in particular

Межстрановой анализ отраслевой производительности труда в 1991-2008 годах

The article presents labor productivity estimates from 1991 to 2008 for 17 countries on industry ISIC. 3 - level. The group of countries includes USA, Canada, Brazil, Russia, Japan, China, Australia and number of major European economies. The goal is to investigate Russia`s industries comparative progress, productivity gap changes and asses possible sources for technology borrowing. In contrast to previous works analysis captures dynamics of wider country grouping on more detailed industry level. Productivity is calculated as value added per hour worked for the following industries: agriculture, hunting, forestry and fishing (A+B), mining, electricity, gas and water supply (C+E), manufacturing (D), construction (F), wholesale and retail trade, hotels and restaurants (G+H), transport (I) and others (J-P)

Оценка догоняющего развития на уровне стран и регионов: методический комментарий

The author shows that different approaches to income comparisons both on country and regional levels may lead to conflicting conclusions concerning the catching-up. It is argued that current PPPs (Instead of constant PPPs) give more reliable picture of per capita GDP convergence process on country level. Based on the case of Russian regions it is showed that different ways of converting incomes into spatially consistent prices also lead to significantly different results. The need of computing (current) regional PPPs is emphasized

Производительность труда в отраслях обрабатывающей промышленности России: динамика и межстрановые сопоставления

The article analyses (per hour) labor productivity dynamics of Russian manufacturing sector in 1995-2008 and presents 2007 year estimates of labor productivity levels for Russia and 9 countries in 13 manufacturing industries. The group of countries includes Russia, USA, Germany, France, CEE countries and China. In contrast to previous works, level comparisons are based on industrial PPPs (which gives more precise estimates), analysis captures wider country grouping and more detailed industry level.Russian manufacturing sector labor productivity is only 18% of US level (in 2007 year). Better stance is in metallurgy (51% of US level) and pulp and paper industry (36% of US level). The worst stance (4-11% of US level) is in wood products, chemical industry and machinery. Considerable productivity gaps are the evidence of technological weakness of Russian economy, but also it implies the possibility of catching up by means of technology borrowing. Labor productivity level analysis shows that Czech, Hungary, Latvia and the most developed industries of China manufacturing can be considered as perspective sources of technology borrowing for Russia

Межстрановой анализ отраслевой производительности труда и душевого ВВП в 1991-2008 годах

The work presents labor productivity estimates from 1991 to 2008 for 17 countries on industry ISIC. 3 – level (calculations for the whole economy-level last till 2012 year). The group of countries includes USA, Canada, Brazil, Russia, Japan, China, Australia and number of major European econo-mies. The goal is to investigate Russia`s industries comparative progress, productivity gap changes and asses possible sources for technology borrowing. In contrast to previous works analysis captures dy-namics of wider country grouping on more detailed industry level. Productivity is calculated as value added per hour worked for the following industries: agriculture, hunting, forestry and fishing (A+B), mining, electricity, gas and water supply (C+E), manufacturing (D), construction (F), wholesale and re-tail trade, hotels and restaurants (G+H), transport (I) and others (J-P). After period of transition Russia has reached own 1991`s level of productivity (per worker) and per capita GDP in 2004 and 2005-2006 years respectively. But if we measure Russia`s productivity or per capita GDP in terms of US levels, the question of reaching 1991 level has no unequivocal answer. It depends on preferred estimation methodology of catching up development, more precisely, on way of converting productivity and per capita GDP to common currency: use general accepted constant prices and constant PPP methodology (as in ((Maddison, 1995) and OECD, BLS USA, PWT calculations), or use current (benchmark) PPPs (WB data) to asses relative (to US) performance of Russia. Reasons of faster per capita GDP growth comparing to labor productivity in Russia in 2000s are explained. In 2008 productivity (per hour) gap between Russia and US was approximately threefold, com-paring with European countries – twofold. Sectoral productivity analysis shows that such a differences in some industries are even greater: up to 4 times in agriculture and transportation comparing with US and 13.5 times in mining and energy sectors (C+E I.S.I.C.) comparing with Norway. Such productivity gaps are the evidence of technological weakness of Russian economy, but also it implies the possibility of catching up by means of technology borrowing. Perspective sources for technology borrowing are identified for Russia`s industries. To asses Russia`s gap in agricultural efficiency and estimate perspective sources for technology borrowing, more detailed comparative analysis is done using indicators such as output per worker, output per hectare of arable land, labor intensity (workers per hectare of arable land), capital intensity and others

Эконометрический анализ динамики российских паевых инвестиционных фондов в кризисный и посткризисный периоды

Using estimated CAPM-models portfolio risks of Russian mutual funds are analyzed. Two questions are considered: how did mutual funds portfolio risks change during the crisis and postcrisis periods; did portfolio managers successfully fit the portfolio structure depending on market conditions? Analysis shows that portfolio risks of majority of funds were constant during the crisis period or even greater, than at precrisis period. This fact conflicts with right active management strategy. Despite the general bad performance of mutual funds, some true active funds were identified. Also it was confirmed (as in previous works) that as whole mutual funds returns do not outperform of that of market index (MICEX) and portfolio managers do not control risk properly in different market conditions

Эконометрический анализ динамики российских паевых инвестиционных фондов в кризисный и посткризисный периоды

Using estimated CAPM-models portfolio risks of Russian mutual funds are analyzed. Two questions are considered: how did mutual funds portfolio risks change during the crisis and postcrisis periods; did portfolio managers successfully fit the portfolio structure depending on market conditions? Analysis shows that portfolio risks of majority of funds were constant during the crisis period or even greater, than at precrisis period. This fact conflicts with right active management strategy. Despite the general bad performance of mutual funds, some true active funds were identified. Also it was confirmed (as in previous works) that as whole mutual funds returns do not outperform of that of market index (MICEX) and portfolio managers do not control risk properly in different market conditions

Оценка догоняющего развития на уровне стран и регионов: методический комментарий

The author shows that different approaches to income comparisons both on country and regional levels may lead to conflicting conclusions concerning the catching-up. It is argued that current PPPs (Instead of constant PPPs) give more reliable picture of per capita GDP convergence process on country level. Based on the case of Russian regions it is showed that different ways of converting incomes into spatially consistent prices also lead to significantly different results. The need of computing (current) regional PPPs is emphasized

Душевой ВВП и производительность труда в России: было ли догоняющее развитие?

Using Russian data, it is showт that different assessment techniques of cathing-up development lead to opposite conclusions: according to current PPPs we have seen the cathing-up in per capita GDP during 1990-2012 period, but according to constant PPPs – we have not. It is argued that current PPPs give more reliable picture of comparative welfare (per capita GDP) dynamics rather than constant PPPs. The author shows that substitution effect explains more than the half of the Russia-US per capita GDP gap reduction in 2005-2012 period. Constant PPPs are preferred for comparisons of productivity dynamics. According to constant PPPs in 2012 Russia had bigger productivity gap (to US) than that in 1991 year