Spatial Memory Drives Foraging Strategies of Wolves, but in Highly Individual Ways

The ability of wild animals to navigate and survive in complex and dynamic environments depends on their ability to store relevant information and place it in a spatial context. Despite the centrality of spatial memory, and given our increasing ability to observe animal movements in the wild, it is perhaps surprising how difficult it is to demonstrate spatial memory empirically. We present a cognitive analysis of movements of several wolves (Canis lupus) in Finland during a summer period of intensive hunting and den-centered pup-rearing. We tracked several wolves in the field by visiting nearly all GPS locations outside the den, allowing us to identify the species, location and timing of nearly all prey killed. We then developed a model that assigns a spatially explicit value based on memory of predation success and territorial marking. The framework allows for estimation of multiple cognitive parameters, including temporal and spatial scales of memory. For most wolves, fitted memory-based models outperformed null models by 20 to 50% at predicting locations where wolves chose to forage. However, there was a high amount of individual variability among wolves in strength and even direction of responses to experiences. Some wolves tended to return to locations with recent predation success-following a strategy of foraging site fidelity-while others appeared to prefer a site switching strategy. These differences are possibly explained by variability in pack sizes, numbers of pups, and features of the territories. Our analysis points toward concrete strategies for incorporating spatial memory in the study of animal movements while providing nuanced insights into the behavioral strategies of individual predators.Peer reviewe

Technological diversity and access of Russian regional enterprises to advanced manufacturing technologies

Формирование технологической базы производства и ее систематическое обновление являются важнейшими факторами укрепления конкурентоспособности компаний и одним из ключевых условий устойчивого экономического роста. Вместе с тем распространение в российских регионах передовых производственных решений, определяющих эффективность использования имеющейся ресурсной базы, происходит неравномерно. В работе рассматривается разнообразие паттернов использования передовых производственных технологий в группах регионов, которые выделяются на основе применения синтетической классификации, учитывающей базовые параметры экономического развития - структуру производства и занятости и размер подушевого ВРП с поправкой на ценовой фактор. Проанализирована взаимосвязь социально-экономических условий развития территорий и их технологических портфелей. Для достижения этой цели построены и проинтерпретированы индексы технологического разнообразия и самообеспеченности. Исследование выполнено с использованием данных федерального статистического наблюдения за разработкой и использованием ППТ за 2011-2018 гг. Проведенный анализ показал, что самостоятельная разработка технологий не является приоритетной стратегией для большинства регионов страны. Более того, все типы регионов (за исключением части аграрных) демонстрируют приоритетный характер импорта технологий. Постепенно растет спрос на результаты российских разработок, что повышает возможность выхода на рынок технологий научных и образовательных организаций высшего образования при условии формирования устойчивых механизмов трансфера знаний в реальный сектор экономики. Успешные примеры демонстрируют группы развитых регионов с опорой на добывающую и обрабатывающую промышленность и регионы, где есть субъекты-лидеры, обеспечивающие необходимую связку между научной и производственной составляющими. Развитие исследований в данном направлении может включать верификацию полученных результатов посредством использования классификаций регионов, сформированных по иным принципам, нежели близость социально-экономических условий.The development and renewal of production technologies are among the key factors determining the competitiveness and sustainable economic growth of companies. At the same time, the spread of advanced manufacturing technologies (AMT), influencing the effectiveness of resource potential, is uneven across Russian regions. The paper focuses on the diverse application patterns of advanced manufacturing technologies in the groups of regions, classified depending on key parameters of their economic development, i.e., the structure of production and employment as well as gross regional product (GRP) per capita adjusted for price factor. To examine the relationship between socio-economic parameters of regional development and technological portfolios of local enterprises, we analysed the indices of technological diversity and self- reliance of enterprises. For that purpose, we used the data from the national statistical survey on the development and use of AMT by enterprises for 20112018. The conducted analysis indicates that independent technology development is not a priority for most Russian regions. Moreover, all types of regions (except for some agricultural ones) demonstrate the priority of technology imports. However, the demand for national R&D results is gradually growing and increases opportunities for research and educational institutions to enter the technology market, provided there are sustainable mechanisms for transferring the knowledge to the real economy. Successful examples include the developed regions relying on the extractive and manufacturing industries as well as the areas where leading entities managed to link scientific and industrial components. Future studies can focus on testing the obtained results using classifications based on principles other than the similarity of socio-economic conditions of regions.Статья подготовлена в рамках научно-исследовательской работы по теме: «Исследование подходов к формированию статистических индикаторов состояния и динамики развития сферы науки и технологий», выполненной НИУ ВШЭ в 2019 г. (Государственный контракт № 0373100029519000093 от 09 сентября 2019 г.).The paper was written in the scope of research on the topic “Approaches to building statistical indicators of current state and dynamics of scientific and technological development” conducted by the HSE in 2019 (state contract No 0373100029519000093 from 09.09.2019)

Model Selection of Graph Signage Models Using Maximum Likelihood (Student Abstract)

Complex systems across various domains can be naturally modeled as signed networks with positive and negative edges. In this work, we design a new class of signage models and show how to select the model parameters that best fit real-world datasets using maximum likelihood

Fair Stable Matchings Under Correlated Preferences (Student Abstract)

Stable matching models are widely used in market design, school admission, and donor organ exchange. The classic Deferred Acceptance (DA) algorithm guarantees a stable matching that is optimal for one side (say men) and pessimal for the other (say women). A sex-equal stable matching aims at providing a fair solution to this problem. We demonstrate that under a class of correlated preferences, the DA algorithm either returns a sex-equal solution or has a very low sex-equality cost

GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks

Signed networks (networks with positive and negative edges) commonly arise in various domains from molecular biology to social media. The edge signs -- i.e., the graph signage -- represent the interaction pattern between the vertices and can provide insights into the underlying system formation process. Generative models considering signage formation are essential for testing hypotheses about the emergence of interactions and for creating synthetic datasets for algorithm benchmarking (especially in areas where obtaining real-world datasets is difficult). In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in the process of organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology, and, based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, novel biological hypotheses, and realistic benchmark datasets in the realm of gene regulation

Eurasian Economic Union: Achievement and Сhallenges of Integration

The question of Eurasian integration is of great interest to politicians and academics for many reasons. First, it is the only existing and developing integration association in the former Soviet Union. Second, this association is valid in the neighbourhood of the European Union. Thirdly, this association is close to the sphere of important trade and investment interests of China. And finally, it is important that the EAEU was formed after 2014 — the beginning of the “sanctions” period in the life of Russia and the world. Integration in the Eurasian space is a unique case when previously closely interconnected countries within the framework of the central planning system, having survived the collapse of the former economic reality and still being in a protracted transformation process, restore their economic ties in a new market environment. In the case of the CIS, reintegration has not developed for some political reasons, despite available economic conditions. In addition to the analysis of macroeconomic and institutional parameters of the countries, the article presents the results of the classification of the regions of four EAEU countries: Armenia, Belarus, Kazakhstan and Kyrgyzstan. To optimise the cross-country comparison, we simplified the classification by the peculiarities of the administrative division of other EAEU members

Regional Dynamics of Household Income and Consumer Demand in Russia

The analysis of regional income and consumer expenses dynamics is very important to estimate the stability of the economy. The 2015–2018 analysis shows that the economy of the country and households had adapted to the external price shock of 2015, which is confirmed by the following trends. After a decrease in real income and demand for goods (especially non-food ones) in 2015–2016, the situation changed in the beginning of 2018: the demand in most regions increased, although its growth is weaker than the growth of real wages and nominal household income. Most regions by types and federal districts show similar increase, which supports economy’s stability. In 2015–2017 the growth of nominal population income was seen in all types of regions except export-oriented ones (–0.1% in 2017). The highest growth rates were seen in less developed agricultural (+3.0%), medium-developed agricultural and industrial regions (+3.0%) and developed industrial-agricultural (+2.5%). In 2013–2017, about two thirds of the consumer potential were provided by highly developed and developed regions. The three regions with most potential by type were financial and economic centers (27–28% of the total consumer potential in Russia), agricultural-industrial regions (18–20%) and diversified regions (15–16%). The real retail trade turnover in 2017 increased in all types of regions. Real retail sales decreased only in 17 regions. In 2017–2018, the share of food goods in retail trade started decreasing slowly, which indicates gradual restoration of pre-crisis demand structur