Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Canadian Niagara Power Glass Slide - Large Group in Tunnel

A large group of men in hard hats in the tunnel. The names of the men are unknown.

Canadian Niagara Power Company - Large group photo in tunnel

Large group of men standing in the tunnel wearing boots and hard hats.

STRUCTURAL ANALYSIS, LAYER THICKNESS MEASUREMENTS AND MINERALOGICAL INVESTIGATION OF THE LARGE INTERIOR LAYERED DEPOSIT WITHIN GANGES CHASMA, VALLES MARINERIS, MARS

The interior layered deposit (ILD) in Ganges Chasma, Valles Marineris, is a 4.25 km high mound that extends approximately 110 km from west to east. The deposition, deformation, and erosion history of the Ganges ILD records aids in identifying the processes that formed and shaped the Chasma. To interpret structural and geomorphic processes acting on the ILD, multiple layer attitudes and layer thickness transects were conducted on the Ganges ILD. Mineralogical data was analyzed to determine correlations between materials and landforms. Layer thickness measurements indicate that the majority of layers are between 0.5 m and 4 m throughout the ILD. Three major benches dominate the Ganges ILD. Layer thicknesses increase at the ILD benches, suggesting that the benches are formed from the gradual thickening of layers. This indicates that the benches are depositional features draping over basement topography. Layer attitudes indicate overall shallow dips generally confined to a North-South direction that locally appear to follow bench topography. Layering is disrupted on a scale of 40 m to 150 m in 12 separate locations throughout the ILD. In all locations, underlying layering is disturbed by overlying folded layers in a trough-like geometry. These features are interpreted to have formed as submarine channels in a lacustrine setting, subsequently infilled by sediments. Subsequently, the channels were eroded to the present topography, resulting in the thin, curved layering observed. Data cannot conclusively support one ILD formation hypothesis, but does indicate that the Ganges ILD postdates Chasma formation. The presence of water altered minerals, consistently thin layering, and layer orientations provide strong evidence that the ILD formed in a lacustrine setting.

Photograph - Woman Standing by a Large Tree Trunk

A woman wearing a hat and holding a small purse. She is standing next to a large tree trunk and there are benches behind her.

Safe Handling of Large Animals

The article discusses improving welfare by reducing fear by studying: Animal Sensory Perception, Animal Behavior Patterns, Animal Habituation and Temperament, Effects of Previous Handling, Training Animals, Training Time and Temperament, Genetic Effects on Handling, Handling of escaped Animals, Facilities, Aggression in Grazing Animals, Inherent Danger of Large Animals, Cattle and Car Accidents.

Assessing the Reproducibility of Clustering of Molecular Dynamics Conformations on Self-Organizing Maps

The goal of most clustering algorithms is to find the optimal number of clusters (i.e. fewest number of clusters). However, analysis of molecular conformations of biological macromolecules obtained from computer simulations may benefit from a larger array of clusters. The Self-Organizing Map (SOM) clustering method has the advantage of generating large numbers of clusters, but often gives ambiguous results. In this work, SOMs have been shown to be reproducible when the same conformational dataset is independently clustered multiple times (~100), with the help of the Cramérs V-index (C_v). The ability of C_v to determine which SOMs are reproduced is generalizable across different SOM source codes. The conformational ensembles produced from MD (molecular dynamics) and REMD (replica exchange molecular dynamics) simulations of the penta peptide Met-enkephalin (MET) and the 34 amino acid protein human Parathyroid Hormone (hPTH) were used to evaluate SOM reproducibility. The training length for the SOM has a huge impact on the reproducibility. Analysis of MET conformational data definitively determined that toroidal SOMs cluster data better than bordered maps due to the fact that toroidal maps do not have an edge effect. For the source code from MATLAB, it was determined that the learning rate function should be LINEAR with an initial learning rate factor of 0.05 and the SOM should be trained by a sequential algorithm. The trained SOMs can be used as a supervised classification for another dataset. The toroidal 10×10 hexagonal SOMs produced from the MATLAB program for hPTH conformational data produced three sets of reproducible clusters (27%, 15%, and 13% of 100 independent runs) which find similar partitionings to those of smaller 6×6 SOMs. The χ^2 values produced as part of the C_v calculation were used to locate clusters with identical conformational memberships on independently trained SOMs, even those with different dimensions. The χ^2 values could relate the different SOM partitionings to each other.

Single-Molecule Magnets and Multifunctional Molecular Magnetic Materials Based on Polynuclear Metal Complexes

Our work on single molecule magnets and multifunctional magnetic materials is presented in four projects. In the first project we show for first time that heteroatomic-type pseudohalides, such as OCN-, can be employed as structure-directing ligands and ferromagnetic couplers in higher oxidation state metal cluster chemistry. The initial use of cyanato groups in Mn cluster chemistry has afforded structurally interesting MnII/III14 (1) and MnII/III/IV16 (2) clusters in which the end-on bridging cyanates show a preference in binding through their O-atom. The Mn14 compound shows entirely visible out-of-phase alternating currect signals below 5 K and large hysteresis loops below 2 K. Furthermore, the amalgamation of azido groups with the triethanolamine tripodal ligand in manganese carboxylate cluster chemistry has led to the isolation of a new ferromagnetic, high-nuclearity and mixed-valence MnII/III15Na2 (3) cluster with a large ground-state spin value of S = 14. In the second project we demonstrate a new synthetic route to purely inorganic-bridged, transition metal-azido clusters [CoII7 (4) and NiII7 (5)] and coordination polymers [{FeII/III2}n (6)] which exhibit strong ferromagnetic, SMM and long-range magnetic ordering behaviors. We also show that access to such a unique ferromagnetic class of inorganic, N-rich and O-free materials is feasible through the use of Me3SiN3 as the azido-ligand precursor without requiring the addition of any organic chelating/bridging ligand. In the last projects we have tried to bring together molecular magnetism and optics via the synthesis of multifunctional magnetic materials based on 3d- or 4f-metal ions. We decided to approach such challenge from two different directions: firstly, in our third project, by the deliberate replacement of non-emissive carboxylato ligands in known 3d-SMMs with their fluorescent analogues, without perturbing the metal-core structure and SMM properties (complexes 7, 8, and 9). The second route (last project) involves the use of naphthalene or pyridine-based polyalcohol bridging ligands for the synthesis of new polynuclear LnIII metal clusters (Ln = lanthanide) with novel topologies, SMM behaviors and luminescent properties arising from the increased efficiency of the “antenna” organic group. This approach has led us to the isolation of two new families of LnIII8 (complexes 10-13) and LnIII4 (complexes 14-20) clusters.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.