This book presents the history of globalization as a network-based story in the context of Big History. Departing from the traditional historic discourse, in which communities, cities, and states serve as the main units of analysis, the authors instead trace the historical emergence, growth, interconnection, and merging of various types of networks that have gradually encompassed the globe. They also focus on the development of certain ideas, processes, institutions, and phenomena that spread through those networks to become truly global.
The book specifies five macro-periods in the history of globalization and comprehensively covers the first four, from roughly the 9th – 7th millennia BC to World War I. For each period, it identifies the most important network-related developments that facilitated (or even spurred on) such transitions and had the greatest impacts on the history of globalization.
By analyzing the world system's transition to new levels of complexity and connectivity, the book provides valuable insights into the course of Big History and the evolution of human societies.
Higher School of Economics (HSE) and supported by the Information Retrieval Specialist Group at the British Computer Society (BCS–IRSG). The conference was held during March 24–27, 2013, in Moscow, Russia – the easternmost location in the history of the ECIR series. ECIR 2013 received a total of 287 submissions in three categories: 191 full papers, 78 posters, and 18 demonstrations. The geographical distribution of the submissions is as follows: 70% were from Europe (including 9% from Russia), 17% from Asia, 12% from North and South America, and 3% from the rest of the world. All submissions were reviewed by at least three members of an international two-tier Program Committee. Of the papers submitted to the main research track, 30 were selected for oral presentation and 25 for poster/short presentation (16% and 13%, respectively, hence a 29% acceptance rate). In addition, 38 posters (49%) and 10 demonstrations (56%) were accepted. The accepted contributions represent the state of the art in information retrieval, cover a diverse range of topics, propose novel applications, and indicate promising directions for future research. Out of accepted contributions, 66% have a student as the primary author. We gratefully thank all Program Committee members for their time and efforts ensuring a high-quality level of the ECIR 2013 program. Additionally, ECIR 2013 hosted four tutorials and two workshops covering various IR-related topics. We express our gratitude to the Workshop Chair, Evgeniy Gabrilovich, and the Tutorial Chair, Djoerd Hiemstra, and the members of their committees.
– Searching the Web of Data
– Practical Online Retrieval Evaluation
– Cross-Lingual Probabilistic Topic Modeling and Its Applications in Information
– Distributed Information Retrieval and Applications
– From Republicans to Teenagers: Group Membership and Search (GRUMPS)
– Integrating IR Technologies for Professional Search
The conference included a Mentoring Program and Doctoral Consortium.
We thank Mikhail Ageev and Hideo Joho and Dmitriy Ignatov, respectively, for coordinating these activities.
We would like to thank our invited speakers – Mor Naaman (Rutgers University, Social Media Information Lab) and the winner of the Karen Sparck Jones award. The Industry Day took place on the final day of the conference and featured a bright assortment of talks given by prominent researchers and practitioners: Paul Ogilvie (LinkedIn), Hilary Mason (bitly), Antonio Gulli (Bing), Andrey Kalinin (Mail.Ru), Jimmy Lin (Twitter/University of Maryland), Marc Najork (Microsoft Research), and Andrey Styskin (Yandex), to whom we express our gratitude. We appreciate generous financial support from Yandex and HSE, as well as from our sponsorsMail.Ru and Russian Foundation for Basic Research (platinum level), Google and ABBYY
This book offers a concise yet thorough introduction to the notion of moduli spaces of complex algebraic curves. Over the last few decades, this notion has become central not only in algebraic geometry, but in mathematical physics, including string theory, as well.
The book begins by studying individual smooth algebraic curves, including the most beautiful ones, before addressing families of curves. Studying families of algebraic curves often proves to be more efficient than studying individual curves: these families and their total spaces can still be smooth, even if there are singular curves among their members. A major discovery of the 20th century, attributed to P. Deligne and D. Mumford, was that curves with only mild singularities form smooth compact moduli spaces. An unexpected byproduct of this discovery was the realization that the analysis of more complex curve singularities is not a necessary step in understanding the geometry of the moduli spaces.
The book does not use the sophisticated machinery of modern algebraic geometry, and most classical objects related to curves – such as Jacobian, space of holomorphic differentials, the Riemann-Roch theorem, and Weierstrass points – are treated at a basic level that does not require a profound command of algebraic geometry, but which is sufficient for extending them to vector bundles and other geometric objects associated to moduli spaces. Nevertheless, it offers clear information on the construction of the moduli spaces, and provides readers with tools for practical operations with this notion.
Based on several lecture courses given by the authors at the Independent University of Moscow and Higher School of Economics, the book also includes a wealth of problems, making it suitable not only for individual research, but also as a textbook for undergraduate and graduate coursework.
This book explores the theory and application of locally nilpotent derivations, which is a subject of growing interest and importance not only among those in commutative algebra and algebraic geometry, but also in fields such as Lie algebras and differential equations. The author provides a unified treatment of the subject, beginning with 16 First Principles on which the entire theory is based. These are used to establish classical results, such as Rentschler s Theorem for the plane, right up to the most recent results, such as Makar-Limanov s Theorem for locally nilpotent derivations of polynomial rings. Topics of special interest include: progress in the dimension three case, finiteness questions (Hilbert s 14th Problem), algorithms, the Makar-Limanov invariant, and connections to the Cancellation Problem and the Embedding Problem. The reader will also find a wealth of pertinent examples and open problems and an up-to-date resource for research.
This is the second part of a 2-year course of abstract algebra for students beginning a professional study of higher mathematics.1 This textbook is based on courses given at the Independent University of Moscow and at the Faculty of Mathematics at the National Research University Higher School of Economics. In particular, it contains a large number of exercises that were discussed in class, some of which are provided with commentary and hints, as well as problems for independent solution that were assigned as homework.Working out the exercises is of crucial importance in understanding the subject matter of this book.
This book is the first volume of an intensive “Russian-style” two-year graduate course in abstract algebra, and introduces readers to the basic algebraic structures – fields, rings, modules, algebras, groups, and categories – and explains the main principles of and methods for working with them.
The course covers substantial areas of advanced combinatorics, geometry, linear and multilinear algebra, representation theory, category theory, commutative algebra, Galois theory, and algebraic geometry – topics that are often overlooked in standard undergraduate courses.
This textbook is based on courses the author has conducted at the Independent University of Moscow and at the Faculty of Mathematics in the Higher School of Economics. The main content is complemented by a wealth of exercises for class discussion, some of which include comments and hints, as well as problems for independent study.
This book constitutes the proceedings of the Third International Conference on Analysis of Images, Social Networks and Texts, AIST 2014, held in Yekaterinburg, Russia, in April 2014. The 11 full and 10 short papers were carefully reviewed and selected from 74 submissions. They are presented together with 3 short industrial papers, 4 invited papers and tutorials. The papers deal with topics such as analysis of images and videos; natural language processing and computational linguistics; social network analysis; machine learning and data mining; recommender systems and collaborative technologies; semantic web, ontologies and their applications; analysis of socio-economic data.
This book constitutes the proceedings of the Fourth International Conference on Analysis of Images, Social Networks and Texts, AIST 2015, held in Yekaterinburg, Russia, in April 2015. The 24 full and 8 short papers were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on analysis of images and videos; pattern recognition and machine learning; social network analysis; text mining and natural language processing.
This book constitutes the proceedings of the 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, held in Yekaterinburg, Russia, in April 2016. The 23 full papers, 7 short papers, and 3 industrial papers were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on machine learning and data analysis; social networks; natural language processing; analysis of images and video.
This volume contains the refereed proceedings of the 6th International Conference on Analysis of Images, Social Networks, and Texts (AIST 2017)1. The previous conferences during 2012–2016 attracted a significant number of students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and social networks. The broad scope of AIST made it an event where researchers from different domains, such as image and text processing, exploiting various data analysis techniques, can meet and exchange ideas. We strongly believe that this may lead to cross fertilisation of ideas between researchers relying on modern data analysis machinery. Therefore, AIST brought together all kinds of applications of data mining and machine learning techniques. The conference allowed specialists from different fields to meet each other, present their work, and discuss both theoretical and practical aspects of their data analysis problems. Another important aim of the conference was to stimulate scientists and people from industry to benefit from the knowledge exchange and identify possible grounds for fruitful collaboration. The conference was held during July 27–29, 2017. The conference was organised in Moscow, the capital of Russia, on the campus of Moscow Polytechnic University. This year, the key topics of AIST were grouped into six tracks: 1. General topics of data analysis chaired by Sergei Kuznetsov (Higher School of Economics, Russia) and Amedeo Napoli (LORIA, France) 2. Natural language processing chaired by Natalia Loukachevitch (Lomonosov Moscow State University, Russia) and Alexander Panchenko (University of Hamburg, Germany) 3. Social network analysis chaired by Stanley Wasserman (Indiana University, USA) 4. Analysis of images and video chaired by Victor Lempitsky (Skolkovo Institute of Science and Technology, Russia) and Andrey Savchenko (Higher School of Economics, Russia) 5. Optimisation problems on graphs and network structures chaired by Panos Pardalos (University of Florida, USA) and Michael Khachay (IMM UB RAS and Ural Federal University, Russia) 6. Analysis of dynamic behaviour through event data chaired by Wil van der Aalst (Eindhoven University of Technology, The Netherlands) and Irina Lomazova (Higher School of Economics, Russia) One of the novelties this year was the introduction of a new specialised track on process mining (Track 6).
This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes:The increasing availability of electronic text data resources relating to Science, Technology & Innovation (ST&I) The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets.
Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of “Big Data” analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI. Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development. A decade ago, we demeaned Management of Technology (MOT) as somewhat selfsatisfied and ignorant. Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy. CTI, Tech Mining, and FIP are changing that.