Array DBMS and Satellite Imagery: Towards Big Raster Data in the Cloud
Nowadays environmental science experiences tremendous growth of raster data: N-dimensional (N-d) arrays coming mainly from numeric simulation and Earth remote sensing. An array DBMS is a tool to streamline raster data processing. However, raster data are usually stored in files, not in databases. Moreover, numerous command line tools exist for processing raster files. This paper describes a distributed array DBMS under development that partially delegates raster data processing to such tools. Our DBMS offers a new N-d array data model to abstract from the files and the tools and processes data in a distributed fashion directly in their native file formats. As a case study, popular satellite altimetry data were used for the experiments carried out on 8- and 16-nodes clusters in Microsoft Azure Cloud. New array DBMS is up to 70× faster than SciDB which is the only freely available distributed array DBMS to date.
This volume contains the refereed proceedings of the 18th international conference on Mathematical Optimization Theory and Operations Research (MOTOR 2019)1 held during July 8–12, 2019, near Ekaterinburg, Russia. The conference brings together a wide research community in the fields of mathematical programming and global optimization, discrete optimization, complexity theory and combinatorial algorithms, optimal control and games, and their applications in relevant practical problems of operations research, mathematical economy, and data analysis
The coursebook is designed for students to acquire, practice, and master their communicative competence in academic writing in English, the focus being on fundamental and applied mathematics and computer science. The target of the book is to teach students to write research project proposals of their term papers, senior theses, and dissertations in the format of a research article which could prospectively be published in a Scopus- or We-of-Science-indexed journals. The book covers both academic writing and academic speaking, i.e. presenting research at conferences and defences.
The materials employed in the book are research articles published in international peer-reviewed journals, both full-text and excerpts.
The target audience comprises undergraduate students majoring in IT, fundamental and applied mathematics, and cyber- and information security. The book could also be of interest to students majoring in other STEM areas, both at the undergraduate and graduate levels.
Relativisation involves dependencies which, although unbounded, are constrained with respect to certain island domains. The Lambek calculus L can provide a very rudimentary account of relativisation limited to unbounded peripheral extraction; the Lambek calculus with bracket modalities Lb can further condition this account according to island domains. However in naïve parsing/theorem-proving by backward chaining sequent proof search for Lb the bracketed island domains, which can be indefinitely nested, have to be specified in the linguistic input. In realistic parsing word order is given but such hierarchical bracketing structure cannot be assumed to be given. In this paper we show how parsing can be realised which induces the bracketing structure in backward chaining sequent proof search with Lb.
The number of space objects will grow several times in a few years due to the planned launches of constellations of thousands microsatellites. It leads to a significant increase in the threat of satellite collisions. Spacecraft must undertake collision avoidance maneuvers to mitigate the risk. According to publicly available information, conjunction events are now manually handled by operators on the Earth. The manual maneuver planning requires qualified personnel and will be impractical for constellations of thousands satellites. In this paper we propose a new modular autonomous collision avoidance system called "Space Navigator". It is based on a novel maneuver optimization approach that combines domain knowledge with Reinforcement Learning methods.
We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ∼0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.
We investigate the complexity consequences of adding pointer arithmetic to separation logic. Specifically, we study an extension of the points-to fragment of symbolic-heap separation logic with sets of simple “difference constraints” of the form x≤y+k
, where x and y are pointer variables and k is an integer offset. This extension can be considered a practically minimal language for separation logic with pointer arithmetic.
Most significantly, we find that, even for this minimal language, polynomial-time decidability is already impossible: satisfiability becomes NP
-complete, while quantifier-free entailment becomes coNP-complete and quantified entailment becomes ΠP2-complete (where ΠP2
is the second class in the polynomial-time hierarchy).
However, the language does satisfy the small model property, meaning that any satisfiable formula has a model, and any invalid entailment has a countermodel, of polynomial size, whereas this property fails when richer forms of arithmetical constraints are permitted.
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).