Abstract: Social networks provide an opportunity to form communities of people that share their interests on a regular basis (circles of fans of different music, books, kinds of sports, etc.). Every community manifests these interests creating lots of linguistic data to attract new followers to certain pages and support existing clusters of users. In the present article, we suggest a model of retrieving such pages that attract users with similar interests, from a large collection of pages. We test our model on three types of pages manually retrieved from the social network Vkontakte and classified as interesting for a. football fans, b. vegetarians, c. historical reenactors. We use such machine learning classifiers as Naive Bayes, SVM, Logistic Regression, Decision Trees to compare their performance with the performance of our system. It appears that the mentioned classifiers can hardly retrieve (i.e. single out) pages with a particular interest that form a small collection of 30 samples from a collection as large as 4,090 samples. In particular, our system exceeds their best result (F1-score=0.65) and achieves F1-score of 0.72.

Abstract: Models of information spread in social networks are widely used to explore the drivers of content contagion and to predict the effect of new information messages. Most of the existing models (aggregated as SIR-like or network-based as independent cascades) use the assumption of homogeneity of an audience. However, to make a model plausible for a description of real-world processes and to measure the accumulated impact of information on individuals, one needs to personalize the characteristics of users as well as sources of information. In this paper, we propose an approach to data-driven simulation of information spread in social networks which combines a set of different models in a unified framework. It includes a model of a user (including sub-models of reaction and daily activity), a model of message generation by information source and a model of message transfer within a user network. The parameters of models (e.g. for different types of agents) are identified by data from the largest Russian social network vk.com. For this study, we collected the network of users associated with charity community (~33.7 million nodes). To tackle with huge size of networks, we implement-ed parallel version of modeling framework and tested it on the Lomonosov supercomputer. We identify key parameters of models that may be tuned to re-produce observable behavior and show that our approach allows to simulate aggregated dynamics of reactions to a series of posts as a combination of individual responses.

Abstract: The network structure of communities in social media significantly affects diffusion processes which implement positive or negative information influence on social media users. Some of the thematic communities in online social networks may provide illegal services or information in them may cause undesired psychological effects; moreover, the topology of such communities and behavior of their members are influenced by a thematic. Nevertheless, recent research does not contain enough detail about the particularities of thematic communities formation, or about the topological properties of underlying friendship networks. To address this gap, in this study we analyze structure of communities of different types, namely, carders, commercial sex workers, substance sellers and users, people with radical political views, and compare them to the 'normal' communities (without a single narrow focus). We discovered that in contrast to ordinary communities which have positive assortativity (as expected for social networks), specific thematical communities are significantly disassortative. Types of anomalous communities also differ not only in content but in structure. The most specific are the communities of radicalized individuals: it was shown that they have the highest connectivity and the larger part of nodes within a friendship graph.

Abstract: Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex networks, detecting nodes that lack access from/to points of interest in a given city. Furthermore, we introduce a greedy algorithm that is able to recommend improvements to the structure of a city by suggesting where points of interest are to be placed. We contribute to a thorough process to deal with complex networks, including mathematical modeling and algorithmic innovation. The set of our contributions introduces a systematic manner to treat a recurrent problem of broad interest in cities.

Abstract: Currently, state-of-the-art algorithms for generating 3-D Delaunay tessellations are incremental. Thus, their execution costs depend on the order of point insertion. This work evaluates three point-insertion sequences in incremental algorithms for generating 3-D Delaunay tessellations. Specifically, an incremental algorithm with point-insertion order given by the cut-longest-edge kd-tree is evaluated against the BRIO-Hilbert order in conjunction with spatial middle and median policies employed in the 4.11 version of the Computational Geometry Algorithms Library. The results of execution and storage costs of these three algorithms are analyzed experimentally. It follows that the incremental algorithm with a point-insertion sequence in the order given by the BRIO-Hilbert order with spatial middle policy employed in the latest version of the Computational Geometry Algorithms Library shows lower execution and storage costs than the two other algorithms evaluated.

Abstract: For knowledge graph completion, translation-based methods such as Trans(E and H) are promising, which embed knowledge graphs into continuous vector spaces and construct translation operation between head and tail entities. However, TransE and TransH still have limitations in preserving mapping properties of complex relation facts for knowledge graphs. In this paper, we propose a novel translation-based method called translation on generalized hyperplanes (TransGH), which extends TransH by defining a generalized hyperplane for entities projection. TransGH projects head and tail embeddings from a triplet into a generalized relation-specific hyperplane determined by a set of basis vectors, and then fulfills translation operation on the hyperplane. Compared with TransH, TransGH can capture more fertile interactions between entities and relations, and simultaneously has strong expression in mapping properties for knowledge graphs. Experimental results on two tasks, link prediction and triplet classification, show that TransGH can significantly outperform the state-of-the-art embedding methods.

Abstract: Tensors analysis has become a popular tool for solving problems in computational neuroscience, pattern recognition and signal processing. Similar to the two-dimensional case, algorithms for multidimensional data consist of basic operations accessing only a subset of tensor data. With multiple offsets and step sizes, basic operations for subtensors require sophisticated implementations even for entrywise operations. In this work, we discuss the design and implementation of optimized higher-order functions that operate entrywise on tensors and subtensors with any non-hierarchical storage format and arbitrary number of dimensions. We propose recursive multi-index algorithms with reduced index computations and additional optimization techniques such as function inlining with partial template specialization. We show that single-index implementations of higher-order functions with subtensors introduce a runtime penalty of an order of magnitude than the recursive and iterative multi-index versions. Including data- and thread-level parallelization, our optimized implementations reach 68% of the maximum throughput of an Intel Core i9-7900X. In comparison with other libraries, the average speedup of our optimized implementations is up to 5x for map-like and more than 9x for reduce-like operations. For symmetric tensors we measured an average speedup of up to 4x.

Abstract: Pseudo-random sequences exhibit interesting properties with application in many and distinct areas ranging from reliable communications to number generation or cryptography. Inside the family of decimation-based sequence generators, the modified self-shrinking generator (an improved version of the self-shrinking generator) is one of its best-known elements. In fact, such a generator divides the PN-sequence produced by a maximum-length LFSR into groups of three bits. When the sum of the first two bits in a group is one, then the generator returns the third bit, otherwise the bit is discarded. In this work, we introduce a generalization of this generator, where the PN-sequence is divided into groups of t bits, t ≥ 3. It is possible to check that the properties of the output sequences produced by this family of generators have the same or better properties than those of the classic modified self-shrunken sequences. Moreover, the number of sequences generated by this new family with application in stream cipher cryptography increases dramatically.

Abstract: Today, the so called supermarket approach is used for trading Cloud services on Cloud markets. Thereby, consumers purchase Cloud services at fixed prices without negotiation. More dynamic Cloud markets are emerging as e.g. the recent development of the Amazon EC2 spot market shows - with spot blocks and spot fleet management. Hence, autonomous Bazaar-based negotiations are a promising approach for trading Cloud services on future Cloud markets. Thereby, market participants negotiate the characteristics of Cloud services which are described in Service Level Agreements (SLAs). Specifications such as the WS-Agreement Negotiation standard foster the development of such Bazaar-based Cloud markets. In this paper we present a scientific simulation environment for the simulation of Bazaar-based Cloud markets which conforms to the WS-Agreement Negotiation standard. A two-stepped process is required for using the simulation environment: first consumers, intermediaries and providers have to be created, then strategies have to be assigned to them before the result of the simulation can be analyzed. The aim of the simulation environment is to support market participants during the evaluation of their negotiation strategies.

Abstract: In this talk, we discuss our recent efforts on developing efficient open-access computational tools for poroelasticity. In particular, we examine (1) the newly added Matlab modules to our code package DarcyLite; (2) our new efforts on C++ modules for deal.II package. Several new finite element solvers have been implemented. These include utilizing the novel Arbogast-Correa elements within the weak Galerkin framework to solve Darcy, elasticity, and poroelasticity problems. Numerical simulations will be presented.

Abstract: In this paper, we study linearly first and second order in time, uniquely solvable and unconditionally energy stable numerical schemes to approximate the phase field model of solid-state dewetting problems based on the novel approach SAV (scalar auxiliary variable), a new developed efficient and accurate method for a large class of gradient flows. The schemes are based on the first order Euler method and the second order backward differential formulas(BDF2) for time discretization, and finite element methods for space discretization. It is shown that the schemes are unconditionally stable and the discrete equations are uniquely solvable for all time steps. we present some numerical experiments to validate the stability and accuracy of the proposed schemes.

Abstract: A novel numerical scheme including time and spatial discretization is presented for the coupled Cahn-Hilliard and Navier-Stokes equations in this paper. Variable densities and viscosities are considered in the numerical scheme. By introducing an intermediate velocity in both Cahn-Hilliard equation and the momentum equation, the scheme can keep the discrete energy law. A splitting method based on the pressure stabilization is implemented to solve the Navier-Stokes equation, while the stabilization approach or convex splitting method is used for the Cahn-Hilliard equation. This novel scheme is totally decoupled, linear, unconditionally energy stable for two-phase incompressible flow diffuse interface model. Numerical results demonstrate the validation, accuracy, robustness and discrete energy law of the proposed scheme in this paper.

Abstract: In this paper, we firstly study numerical methods for gas flow simulation in dual-continuum porous media. Typical methods for oil flow simulation in dual-continuum porous media cannot be used straightforward to this kind of simula-tion due to the artificial mass loss caused by the compressibility and the non-robustness caused by the non-linear source term. To avoid these two problems, corrected numerical methods are proposed using mass balance equations and lo-cal linearization of the non-linear source term. The improved numerical methods are successful for the computation of gas flow in the double-porosity double-permeability porous media. After this improvement, temporal advancement for each time step includes three fractional steps: i) advance matrix pressure and frac-ture pressure using the typical computation; ii) solve the mass balance equation system for mean pressures; iii) correct pressures in i) by mean pressures in ii). Numerical results show that mass conservation of gas for the whole domain is guaranteed while the numerical computation is robust.

Abstract: We study the numerical approximation of the incompressible miscible displacement equations on general quadrilateral grids in two dimensions. The flow equation is discretized by multipoint flux mixed finite element method and the transport equation is approximated by discontinuous Galerkin method. First-order convergence for velocity in $L^{\infty}(L^2)$ and concentration in $L^2(H^1)$ is derived. A numerical example is presented to support the theoretical analysis.

Abstract: In the present work we present a system used to simulate crowds in complex urban environments; the system is built in two stages, urban environment generation and pedestrian simulation, for the first stage we integrate the WRLD3D plug-in with real data collected from GPS traces, then we use a hybrid approach done by incorporating steering pedestrian behaviors with the goal of simulating the subtle variations present in real scenarios without needing large amounts of data for those low-level behaviors, such as pedestrian motion affected by other agents and static obstacles nearby. Nevertheless, realistic human behavior cannot be modeled using deterministic approaches, therefore our simulations are both data-driven and sometimes are handled by using a combination of finite state machines (FSM) and fuzzy logic in order to handle the uncertainty of people motion.

Abstract: Roulette Wheel Sampling, sometimes referred to as Fitness Proportionate Selection, is a method to sample from a set of objects each with an associated weight. This paper introduces a distributed version of the method designed for message passing environments. Theoretical bounds are derived to show that the presented method has better scalability than naive approaches. This is verified empirically on a test cluster, where improved speedup is measured. In all tested configurations, the presented method performs better than naive approaches. Through a renumbering step, communication volume is minimized. This step also ensures reproducibility regardless of the underlying architecture.

Abstract: The paper seeks to introduce a new algorithm for computation of interpolating spline surfaces over non-uniform grids with C^2 class continuity, generalizing a recently proposed approach for uniform grids originally based on a special approximation property between biquartic and bicubic polynomials. The algorithm breaks down the classical de Boor’s computational task to systems of equations with reduced size and simple remainder explicit formulas. It is shown that the original algorithm and the new one are numerically equivalent and the latter is up to 50% faster than the classic approach.

Abstract: Refined Isogeometric Analysis (rIGA) is a discretization method used to solve numerical problems governed by partial differential equations (PDEs). Starting from a highly continuous Isogeometric Analysis (IGA) discretization, rIGA reduces the continuity over certain hyperplanes that split the mesh into subdomains. This method maximizes the performance of direct solvers by reducing the continuity until C^0 over selected hyperplanes, which act as separators during the elimination of the degrees of freedom (DoF). By doing so, the solution time and best approximation error are simultaneously improved. In particular, D. Garcia et al. show that rIGA delivers a speedup factor with respect to IGA that is proportional to $p^2$ when solving Laplace based problems in 2D and 3D, with p being the polynomial degree. In this work, we extend rIGA method to solve multi-field problems. We consider incompressible fluid flow problems on bounded domains. They include the pressure and the vectorial velocity of the fluid. We use a spline-based generalization of the Raviart-Thomas finite element spaces to approximate the velocity field. We show that rIGA delivers a reduction in the computational cost when solving incompressible fluid flow problems that asymptotically reaches O(p^2), and it provides better accuracy than C^(p-1) IGA. For multi-field problems, however, we require larger problems to arrive at the asymptotic limit and reach the maximum possible savings since the system involves more equations. In our numerical 2D results, we observe a reduction factor in the computational cost of up to p^2. In 3D, the maximum reproducible problems are in the pre-asymptotic regime, and the maximum observed gain factors are of O(p).

Abstract: We focus on a fast explicit solver for solution of non-stationary problems using L2 projections with isogeometric finite element method. The algorithm has been implemented in Fortran 2008 with MPI and OpenMP frameworks. It enables for parallel multi-core hybrid memory simulations of different time-dependent problems in 3D. We have prepared the solwer framework in a way that enables for direct implementation of the selected Partial Differential Equations and corresponding boundary conditions. The presented package generates output suitable for interfacing with ParaView visualisation software. Finally new implementation is complementary to previously released GALOIS based shared memory code written in C++. Our library manages most of computations, while user has to provide subroutine with equations for Right Hand Side.

Abstract: Mistakes in pedestrian infrastructure design in modern cities decrease transfer comfort for people, impact greenery due to appearance of desire paths, and thus increase the amount of dust in the air because of open ground. These mistakes can be avoided if optimal path networks are created considering behavioral aspects of pedestrian traffic, which is a challenge. In this article, we introduce Ant Road Planner, a new method of computer simulation for estimation and creation of optimal path networks which not only considers pedestrians' behavior but also helps minimize the total length of the paths so that the area is used more efficiently. The method, which includes a modeling algorithm and its software implementation with a user-friendly web interface, makes it possible to predict pedestrian networks for new territories with high precision and detect problematic areas in existing networks. The algorithm was successfully tested on real territories and proved its potential as a decision making support system for urban planners.