Fast Simulation and a Search for Four-top-quark Production with the ATLAS Experiment

The Standard Model of particle physics is one of the most successful theories in physics. However there is still a range of natural phenomena that it is unable to explain. This thesis presents a measurement of Standard Model production of four top quarks, a rare process that is sensitive to influences from potential beyond Standard Model processes, using data from the ATLAS experiment at CERN's Large Hadron Collider (LHC). This thesis also documents a fast simulation software package that was developed for the ATLAS Fast Tracker system, which will be instrumental in adapting to the coming High-Luminosity LHC era.Thesis (MPhil) -- University of Adelaide, School of Physical Sciences, 202

Polarization-Engineering in III-V Nitride Heterostructures: New Opportunities For Device Design

The role of spontaneous and piezoelectric polarization in III-V nitride heterostructure devices is discussed. Problems as well as opportunities in incorporating polarization in abrupt and graded heterojunctions composed of binary, ternary, and quaternary nitrides are outlined.Comment: 7 pages, 5 figure

Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling

Song, Havlin and Makse (2005) have recently used a version of the box-counting method, called the node-covering method, to quantify the self-similar properties of 43 cellular networks: the minimal number $N_V$ of boxes of size $\ell$ needed to cover all the nodes of a cellular network was found to scale as the power law $N_V \sim (\ell+1)^{-D_V}$ with a fractal dimension $D_V=3.53\pm0.26$. We propose a new box-counting method based on edge-covering, which outperforms the node-covering approach when applied to strictly self-similar model networks, such as the Sierpinski network. The minimal number $N_E$ of boxes of size $\ell$ in the edge-covering method is obtained with the simulated annealing algorithm. We take into account the possible discrete scale symmetry of networks (artifactual and/or real), which is visualized in terms of log-periodic oscillations in the dependence of the logarithm of $N_E$ as a function of the logarithm of $\ell$. In this way, we are able to remove the bias of the estimator of the fractal dimension, existing for finite networks. With this new methodology, we find that $N_E$ scales with respect to $\ell$ as a power law $N_E \sim \ell^{-D_E}$ with $D_E=2.67\pm0.15$ for the 43 cellular networks previously analyzed by Song, Havlin and Makse (2005). Bootstrap tests suggest that the analyzed cellular networks may have a significant log-periodicity qualifying a discrete hierarchy with a scaling ratio close to 2. In sum, we propose that our method of edge-covering with simulated annealing and log-periodic sampling minimizes the significant bias in the determination of fractal dimensions in log-log regressions.Comment: 19 elsart pages including 9 eps figure

Confidence sets for network structure

Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific effects. Although maximum-likelihood estimates do not appear consistent in this context, we are able to evaluate confidence sets as a function of different blockmodel partitions, which enables us to qualitatively assess the significance of estimated residual network structure relative to a baseline, which models covariates but lacks block structure.Comment: 17 pages, 3 figures, 3 table

Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus Hurst index

Nonlinear time series analysis aims at understanding the dynamics of stochastic or chaotic processes. In recent years, quite a few methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network. We study the topological properties of horizontal visibility graphs constructed from fractional Brownian motions with different Hurst index $H\in(0,1)$. Special attention has been paid to the impact of Hurst index on the topological properties. It is found that the clustering coefficient $C$ decreases when $H$ increases. We also found that the mean length $L$ of the shortest paths increases exponentially with $H$ for fixed length $N$ of the original time series. In addition, $L$ increases linearly with respect to $N$ when $H$ is close to 1 and in a logarithmic form when $H$ is close to 0. Although the occurrence of different motifs changes with $H$, the motif rank pattern remains unchanged for different $H$. Adopting the node-covering box-counting method, the horizontal visibility graphs are found to be fractals and the fractal dimension $d_B$ decreases with $H$. Furthermore, the Pearson coefficients of the networks are positive and the degree-degree correlations increase with the degree, which indicate that the horizontal visibility graphs are assortative. With the increase of $H$, the Pearson coefficient decreases first and then increases, in which the turning point is around $H=0.6$. The presence of both fractality and assortativity in the horizontal visibility graphs converted from fractional Brownian motions is different from many cases where fractal networks are usually disassortative.Comment: 12 pages, 8 figure

Directional Rumble Strips for Reducing Wrong-Way-Driving Freeway Entries

This report presents evaluation results of directional rumble strips (DRS) designed to deter wrong-way (WW) freeway entries. Mathematical models have been built to identify high-risk locations of WWD. Based on the model, one off-ramp, exit 41 northbound on I-70 was found to have a WW entry probability of 55%. 96 hours of video data were recorded at the chosen off-ramp. Then one pattern of DRS (D3) was implemented on the chosen location with the help of the Illinois Department of Transportation (IDOT). Sound and vibration data were recorded and compared between RW and WW directions for speed ranging from 15 mph to 30 mph. Another 96 hours of video data were recorded after the implementation. The analysis of before and after implementation data showed that the DRS cannot reduce the probability of WWD, but it can warn WW drivers and reduce their speed, which will significantly reduce WWD accidents

Enzyme-powered hollow mesoporous Janus nanomotors

The development of synthetic nanomotors for technological applications in particular for life science and nanomedicine is a key focus of current basic research. However, it has been challenging to make active nanosystems based on biocompatible materials consuming nontoxic fuels for providing self-propulsion. Here, we fabricate self-propelled Janus nanomotors based on hollow mesoporous silica nanoparticles (HMSNPs), which are powered by biocatalytic reactions of three different enzymes: catalase, urease, and glucose oxidase (GOx). The active motion is characterized by a mean-square displacement (MSD) analysis of optical video recordings and confirmed by dynamic light scattering (DLS) measurements. We found that the apparent diffusion coefficient was enhanced by up to 83%. In addition, using optical tweezers, we directly measured a holding force of 64 ± 16 fN, which was necessary to counteract the effective self-propulsion force generated by a single nanomotor. The successful demonstration of biocompatible enzyme-powered active nanomotors using biologically benign fuels has a great potential for future biomedical applications

Degree distribution of the visibility graphs mapped from fractional Brownian motions and multifractal random walks

The dynamics of a complex system is usually recorded in the form of time series, which can be studied through its visibility graph from a complex network perspective. We investigate the visibility graphs extracted from fractional Brownian motions and multifractal random walks, and find that the degree distributions exhibit power-law behaviors, in which the power-law exponent $\alpha$ is a linear function of the Hurst index $H$ of the time series. We also find that the degree distribution of the visibility graph is mainly determined by the temporal correlation of the original time series with minor influence from the possible multifractal nature. As an example, we study the visibility graphs constructed from two Chinese stock market indexes and unveil that the degree distributions have power-law tails, where the tail exponents of the visibility graphs and the Hurst indexes of the indexes are close to the $\alpha\sim H$ linear relationship.Comment: 7 pages, 7 figure

Inhibition of DNA methyltransferases blocks mutant huntingtin-induced neurotoxicity

Although epigenetic abnormalities have been described in Huntington’s disease (HD), the causal epigenetic mechanisms driving neurodegeneration in HD cortex and striatum remain undefined. Using an epigenetic pathway-targeted drug screen, we report that inhibitors of DNA methyltransferases (DNMTs), decitabine and FdCyd, block mutant huntingtin (Htt)-induced toxicity in primary cortical and striatal neurons. In addition, knockdown of DNMT3A or DNMT1 protected neurons against mutant Htt-induced toxicity, together demonstrating a requirement for DNMTs in mutant Htt-triggered neuronal death and suggesting a neurodegenerative mechanism based on DNA methylation-mediated transcriptional repression. Inhibition of DNMTs in HD model primary cortical or striatal neurons restored the expression of several key genes, including Bdnf, an important neurotrophic factor implicated in HD. Accordingly, the Bdnf promoter exhibited aberrant cytosine methylation in mutant Htt-expressing cortical neurons. In vivo, pharmacological inhibition of DNMTs in HD mouse brains restored the mRNA levels of key striatal genes known to be downregulated in HD. Thus, disturbances in DNA methylation play a critical role in mutant Htt-induced neuronal dysfunction and death, raising the possibility that epigenetic strategies targeting abnormal DNA methylation may have therapeutic utility in HD