Growth and Containment of a Hierarchical Criminal Network

We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure. At the same time, law enforcement agents attempt to dismantle the growing organization using pursuit strategies that initiate on the lower level nodes and that unfold as self-avoiding random walks. The global details of the organization are unknown to law enforcement, who must explore the hierarchy node by node. We halt the pursuit when certain local criteria of the network are uncovered, encoding if and when an arrest is made; the criminal network is assumed to be eradicated if the kingpin is arrested. We first analyze recruitment and study the large scale properties of the growing network; later we add pursuit and use numerical simulations to study the eradication probability in the case of three pursuit strategies, the time to first eradication and related costs. Within the context of this model, we find that eradication becomes increasingly costly as the network increases in size and that the optimal way of arresting the kingpin is to intervene at the early stages of network formation. We discuss our results in the context of dark network disruption and their implications on possible law enforcement strategies.Comment: 16 pages, 11 Figures; New title; Updated figures with color scheme better suited for colorblind readers and for gray scale printin

MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met

Time and space integrating acousto-optic folded spectrum processing for SETI

Time and space integrating folded spectrum techniques utilizing acousto-optic devices (AOD) as 1-D input transducers are investigated for a potential application as wideband, high resolution, large processing gain spectrum analyzers in the search for extra-terrestrial intelligence (SETI) program. The space integrating Fourier transform performed by a lens channels the coarse spectral components diffracted from an AOD onto an array of time integrating narrowband fine resolution spectrum analyzers. The pulsing action of a laser diode samples the interferometrically detected output, aliasing the fine resolution components to baseband, as required for the subsequent charge coupled devices (CCD) processing. The raster scan mechanism incorporated into the readout of the CCD detector array is used to unfold the 2-D transform, reproducing the desired high resolution Fourier transform of the input signal

The Czech Republic, 27. 11. -9

Abstract: Our goal is to show on several examples the great progress made in numerical analysis in the past decades together with the principal problems and relations to other disciplines. We restrict ourselves to numerical linear algebra, or, more specifically, to solving Ax = b where A is a real nonsingular n by n matrix and b a real n−dimensional vector, and to computing eigenvalues of a sparse matrix A. We discuss recent developments in both sparse direct and iterative solvers, as well as fundamental problems in computing eigenvalues. The effects of parallel architectures to the choice of the method and to the implementation of codes are stressed throughout the contribution

In-Mold Assembly of Multi-Functional Structures

Combining the recent advances in injection moldable polymer composites with the multi-material molding techniques enable fabrication of multi-functional structures to serve multiple functions (e.g., carry load, support motion, dissipate heat, store energy). Current in-mold assembly methods, however, cannot be simply scaled to create structures with miniature features, as the process conditions and the assembly failure modes change with the feature size. This dissertation identifies and addresses the issues associated with the in-mold assembly of multi-functional structures with miniature components. First, the functional capability of embedding actuators is developed. As a part of this effort, computational modeling methods are developed to assess the functionality of the structure with respect to the material properties, process parameters and the heat source. Using these models, the effective material thermal conductivity required to dissipate the heat generated by the embedded small scale actuator is identified. Also, the influence of the fiber orientation on the heat dissipation performance is characterized. Finally, models for integrated product and process design are presented to ensure the miniature actuator survivability during embedding process. The second functional capability developed as a part of this dissertation is the in-mold assembly of multi-material structures capable of motion and load transfer, such as mechanisms with compliant hinges. The necessary hinge and link design features are identified. The shapes and orientations of these features are analyzed with respect to their functionality, mutual dependencies, and the process cost. The parametric model of the interface design is developed. This model is used to minimize both the final assembly weight and the mold complexity as the process cost measure. Also, to minimize the manufacturing waste and the risk of assembly failure due to unbalanced mold filling, the design optimization of runner systems used in multi-cavity molds for in-mold assembly is developed. The complete optimization model is characterized and formulated. The best method to solve the runner optimization problem is identified. To demonstrate the applicability of the tools developed in this dissertation towards the miniaturization of robotic devices, a case study of a novel miniature air vehicle drive mechanism is presented

Development and application of conformational methodologies: eliciting enthalpic global minima and reaction pathways

2014 Fall.The information granted by assembling the global minimum and low-enthalpy population of a chemical species or ensemble can be utilized to great effect across all fields of chemistry. With this population, otherwise impossible tasks including (but not limited to) reaction pathway characterization, protein folding, protein-ligand docking, and constructing the entropy to characterize free energy surfaces becomes a reasonable undertaking. For very small systems (single molecule with 1-3 torsions) generating the low-enthalpy population is a trivial task. However as the system grows, the task exponentially increases in difficulty. This dissertation will detail the two sides of this problem, generating the low-energy population of larger and more complex species and then utilizing those populations to garner a greater understanding of their systems. The first discussion describes a new model, Surface Editing Molecular Dynamics (SEMD), which aids in accelerating conformational searching by removing minima from the potential energy surface by adding Gaussian functions. Accompanying this new method are a multitude of new tools that can be utilized to aid in molecular dynamics simulations. The first of these tools, named CHILL, performs a projection of unproductive degrees of freedom from the molecular dynamics velocity to smooth atomic motions without artificially constraining those degrees of freedom. Another tool, Conjugate Velocity Molecular Dynamics (CVMD), rigorously generates a list of productive velocities via the biorthogonalization of local modes with a vector representation of previously explored conformational minima. In addition to these tools, a new description of distance in torsional space was developed to provide a robust means of conformational uniqueness. With each of these tools working in concert, the global minimum and associated low-enthalpy population of conformations have been obtained for various benchmark species. The second section discusses the application of conformational searching and the subsequent electronic structure calculations to characterize the reaction pathway for the ruthenium tris(2,2'-bipyridine) photocatalyzed [2+2] cycloaddition of aromatically substituted bis(enones). The APFD hybrid density functional is used along with a 6-311+g* basis and a PCM solvent model. The reaction is computed to proceed through a rate-limited formation of a cyclopentyl intermediate. Lithium tetrafluoroborate is found to facilitate initial bis(enone) reduction as well as final product distribution. In addition, aromatic substituents are found to impact both initial reduction and final product distribution

Memory and performance issues in parallel multifrontal factorizations and triangular solutions with sparse right-hand sides

Nous nous intéressons à la résolution de systèmes linéaires creux de très grande taille sur des machines parallèles. Dans ce contexte, la mémoire est un facteur qui limite voire empêche souvent l’utilisation de solveurs directs, notamment ceux basés sur la méthode multifrontale. Cette étude se concentre sur les problèmes de mémoire et de performance des deux phases des méthodes directes les plus coûteuses en mémoire et en temps : la factorisation numérique et la résolution triangulaire. Dans une première partie nous nous intéressons à la phase de résolution à seconds membres creux, puis, dans une seconde partie, nous nous intéressons à la scalabilité mémoire de la factorisation multifrontale. La première partie de cette étude se concentre sur la résolution triangulaire à seconds membres creux, qui apparaissent dans de nombreuses applications. En particulier, nous nous intéressons au calcul d’entrées de l’inverse d’une matrice creuse, où les seconds membres et les vecteurs solutions sont tous deux creux. Nous présentons d’abord plusieurs schémas de stockage qui permettent de réduire significativement l’espace mémoire utilisé lors de la résolution, dans le cadre d’exécutions séquentielles et parallèles. Nous montrons ensuite que la façon dont les seconds membres sont regroupés peut fortement influencer la performance et nous considérons deux cadres différents : le cas "hors-mémoire" (out-of-core) où le but est de réduire le nombre d’accès aux facteurs, qui sont stockés sur disque, et le cas "en mémoire" (in-core) où le but est de réduire le nombre d’opérations. Finalement, nous montrons comment améliorer le parallélisme. Dans la seconde partie, nous nous intéressons à la factorisation multifrontale parallèle. Nous montrons tout d’abord que contrôler la mémoire active spécifique à la méthode multifrontale est crucial, et que les technique de "répartition" (mapping) classiques ne peuvent fournir une bonne scalabilité mémoire : le coût mémoire de la factorisation augmente fortement avec le nombre de processeurs. Nous proposons une classe d’algorithmes de répartition et d’ordonnancement "conscients de la mémoire" (memory-aware) qui cherchent à maximiser la performance tout en respectant une contrainte mémoire fournie par l’utilisateur. Ces techniques ont révélé des problèmes de performances dans certains des noyaux parallèles denses utilisés à chaque étape de la factorisation, et nous avons proposé plusieurs améliorations algorithmiques. Les idées présentées tout au long de cette étude ont été implantées dans le solveur MUMPS (Solveur MUltifrontal Massivement Parallèle) et expérimentées sur des matrices de grande taille (plusieurs dizaines de millions d’inconnues) et sur des machines massivement parallèles (jusqu’à quelques milliers de coeurs). Elles ont permis d’améliorer les performances et la robustesse du code et seront disponibles dans une prochaine version. Certaines des idées présentées dans la première partie ont également été implantées dans le solveur PDSLin (solveur linéaire hybride basé sur une méthode de complément de Schur). ABSTRACT : We consider the solution of very large sparse systems of linear equations on parallel architectures. In this context, memory is often a bottleneck that prevents or limits the use of direct solvers, especially those based on the multifrontal method. This work focuses on memory and performance issues of the two memory and computationally intensive phases of direct methods, that is, the numerical factorization and the solution phase. In the first part we consider the solution phase with sparse right-hand sides, and in the second part we consider the memory scalability of the multifrontal factorization. In the first part, we focus on the triangular solution phase with multiple sparse right-hand sides, that appear in numerous applications. We especially emphasize the computation of entries of the inverse, where both the right-hand sides and the solution are sparse. We first present several storage schemes that enable a significant compression of the solution space, both in a sequential and a parallel context. We then show that the way the right-hand sides are partitioned into blocks strongly influences the performance and we consider two different settings: the out-of-core case, where the aim is to reduce the number of accesses to the factors, that are stored on disk, and the in-core case, where the aim is to reduce the computational cost. Finally, we show how to enhance the parallel efficiency. In the second part, we consider the parallel multifrontal factorization. We show that controlling the active memory specific to the multifrontal method is critical, and that commonly used mapping techniques usually fail to do so: they cannot achieve a high memory scalability, i.e. they dramatically increase the amount of memory needed by the factorization when the number of processors increases. We propose a class of "memory-aware" mapping and scheduling algorithms that aim at maximizing performance while enforcing a user-given memory constraint and provide robust memory estimates before the factorization. These techniques have raised performance issues in the parallel dense kernels used at each step of the factorization, and we have proposed some algorithmic improvements. The ideas presented throughout this study have been implemented within the MUMPS (MUltifrontal Massively Parallel Solver) solver and experimented on large matrices (up to a few tens of millions unknowns) and massively parallel architectures (up to a few thousand cores). They have demonstrated to improve the performance and the robustness of the code, and will be available in a future release. Some of the ideas presented in the first part have also been implemented within the PDSLin (Parallel Domain decomposition Schur complement based Linear solver) solver