This thesis presents work towards the first measurement of the mass of the W boson (m_W) at ATLAS at the Large Hadron Collider using √s = 7 TeV collision data taken in 2011. The electron energy calibration is presented, including a detailed study of the linearity of the ATLAS electromagnetic calorimeter. Separately, the measurement of the W boson recoil is calibrated using Z boson events. Recoil corrections for the underlying event, pileup, recoil magnitude resolution, recoil angular resolution and recoil response mismodelling are determined, with statistical uncertainties on these corrections corresponding to an estimated uncertainty on m_W of 3.9 MeV. The corrections for calorimeter non-linearity and recoil modelling improve the description of the data, though systematic biases remain. To achieve a precision commensurate with the statistics of the data, these biases will need to be understood.

This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation.

Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed optimizations allow IterStore to reduce the total run time of our ML benchmarks by up to 50×. GeePS is a parameter server that is specialized for deep learning on distributed GPUs. By exploiting the layer-by-layer data access and computation pattern of deep learning, GeePS provides almost linear scalability from single-machine baselines (13× more training throughput with 16 machines), and also supports neural networks that do not fit in GPU memory. MLtuner is a system for automatically tuning the training tunables of ML tasks. It exploits the characteristic that the best tunable settings can often be decided quickly with just a short trial time. By making use of optimization-guided online trial-and-error, MLtuner can robustly find and re-tune tunable settings for a variety of machine learning applications, including image classification, video classification, and matrix factorization, and is over an order of magnitude faster than traditional hyperparameter tuning approaches.

Large integers are used in several key areas such as RSA (Rivest-Shamir-Adleman) public-key cryptographic system and elliptic curve public-key cryptographic system. To achieve higher levels of security requires larger key size and this becomes a limiting factor in prime finite field GF(p) arithmetic using large integers because operations on large integers suffer from the long carry propagation problem. Large integer representation has direct impact on the efficiency of the calculations and the hardware and software implementations. Attempts to use different representations such as residue number systems suffer from their own problems. In this dissertation, we propose a novel and efficient attribute-based large integer representation scheme capable of efficiently representing the large integers that are commonly used in cryptography such as the five NIST primes and the Pierpont primes used in supersingular isogeny Diffie-Hellman (SIDH) used in post-quantum cryptography. Moreover, we propose algorithms for this new representation to perform arithmetic operations such as conversions from and to binary representation, two’s complement, left-shift, numbers comparison, addition/subtraction, modular addition/subtraction, modular reduction, multiplication, and modular multiplication. Extensive numerical simulations and software implementations are done to verify the performance of the new number representation. Results show that the attribute-based large integer arithmetic operations are done faster in our proposed representation when compared with binary and residue number representations. This makes the proposed representation suitable for cryptographic applications on embedded systems and IoT devices with limited resources for better security level. / Graduate / 2020-07-04

by Li Wai Ting. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1987. / Includes bibliographical references.

Large-scale distributed systems and distributed computing are the pillars of IT infrastructure and society nowadays. Robust theoretical principles for designing, building, managing and understanding the interactive behaviours of such systems need to be explored. A promising approach for establishing such principles is to view the session as the key unit for design, execution and verification. Governance is a general term for verifying whether activities meet the specified requirements and for enforcing safe behaviours among processes. This thesis, based on the asynchronous -calculus and the theory of session types, provides a monitoring framework and a theory for validating specifications, verifying mutual behaviours during runtime, and taking actions when noncompliant behaviours are detected. We explore properties and principles for governing large-scale distributed systems, in which autonomous and heterogeneous system components interact with each other in the network to accomplish application goals. This thesis, incorporating lessons from my participation in a substantial practical project, the Ocean Observatories Initiative (OOI), proposes an asynchronous monitoring framework and the process calculus for dynamically governing the asynchronous interactions among distributed multiple applications. We prove that this monitoring model guarantees the satisfaction of global assertions, and state and prove theorems of local and global safety, transparency, and session fidelity. We also study and introduce the semantic mechanisms for runtime session-based governance and the principles of validation of stateful specifications through capturing the runtime asynchronous interactions.

The theory of large deviations can help to shed light on systems in non-equilibrium statistical mechanics and, more generically, on non-reversible stochastic processes. For this purpose, we target trajectories in space time rather than static configurations and study time-extensive observables. This suggests that the details of the evolution law such as the presence of time correlations take on a major role. In this thesis, we investigate selected models with stochastic dynamics that incorporate memory by means of different mechanisms, devise a numerical approach for such models, and quantify to what extent the memory affects the large deviation functionals. The results are relevant for real-world situations, where simplified memoryless (Markovian) models may not always be appropriate. After an original introduction to the mathematics of stochastic processes, we explore, analytically and numerically, an open-boundary zero-range process which incorporates memory by means of hidden variables that affect particle congestion. We derive the exact solution for the steady state of the one-site system, as well as a mean-field approximation for larger one-dimensional lattices. Then, we focus on the large deviation properties of the particle current in such a system. This reveals that the time correlations can be apparently absorbed in a memoryless description for the steady state and the small fluctuation regime. However, they can dramatically alter the probability of rare currents. Different regimes are separated by dynamical phase transitions. Subsequently, we address systems in which the memory cannot be encoded in hidden variables or the waiting-time distributions depend on the whole trajectory. Here, the difficulty in obtaining exact analytical results is exacerbated. To tackle these systems, we have proposed a version of the so-called 'cloning' algorithm for the evaluation of large deviations that can be applied consistently for both Markovian and non-Markovian dynamics. The efficacy of this approach is confirmed by numerical results for some of the rare non-Markovian models whose large deviation functions can be obtained exactly. We finally adapt this machinery to a technological problem, specifically the performance evaluation of communication systems, where temporal correlations and large deviations are important.

This thesis investigates the characteristics and impacts of large wood accumulations within river reaches of different size and style. Four reaches were studied: (i) a wide, braided, headwater reach, characterised by dead wood (Tagliamento River, Italy); (ii) a lower gradient, wide, braided reach, characterised by resprouting wood (Tagliamento River, Italy); (iii) a low gradient, single thread reach with a natural supply of dead wood (Highland Water, UK), and (iv) a low gradient, single thread reach that has been restored by felling trees into the river (River Bure, UK). In each reach, quantities of wood, types of accumulation and their association with sediment retention, landform and propagule bank development were investigated, generating four main findings: 1. There were marked differences in the size and character of large wood accumulations among the four reaches. 2. Retention of fine sediment and organic matter by wood was observed on all four reaches, giving rise to notable spatial heterogeneity in surface sediments. 3. Sediment retention resulted in the development of different landforms among the four reaches. In the two multi-thread reaches, accretion of finer sediment around large wood led to island development. In the naturally-functioning single-thread reach, wood jams spanned the river channel, accumulating sediment and organic matter to produce unvegetated wood jams, and inducing other landforms, notably pools and bars. Such geomorphic heterogeneity was anticipated in the restored reach, but to date this has not significantly occurred. 4. Spatio-temporal variations were observed in propagule abundance and species richness within different wood-related mesohabitats. Higher abundance and species richness were associated with finer, more organic sediments retained within wood accumulations and related mesohabitats. In the restored reach such associations were not statistically significant, further indicating that responses to wood emplacement take longer than the 4 years since restoration. iv Overall, this research has strengthened the evidence concerning the differing nature of wood accumulations in rivers of different size and style, and it has demonstrated the importance of large wood for retaining organic matter and plant propagules, resources essential for riparian vegetation succession and for the success of river restoration efforts.

He, Xu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves [127]-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

This thesis presents the measurement and results of the Z/γ* → μ⁺μ⁻ Drell-Yan triple-differential cross-section, using 20.24 fb⁻¹ of ATLAS data recorded in 2012 at a centre-of-mass energy of √s = 8TeV. The triple-differential cross-section is measured as a function of dimuon invariant mass, m_μμ, dimuon rapidity, γ_μμ , and lepton decay angle in the Collins-Soper frame, cosθ*. These dimensions provide sensitivity to the parton composition of the proton through the parton density functions, PDFs, and the weak effective mixing angle, sin²θeff./W, via the forward-backward asymmetry, A_FB. The measurement is performed on and around the Z-boson's invariant mass peak, 46 < m_μμ < 200 GeV, in a kinematic ducial volume of muon transverse momentum, pT > 20 GeV, and muon pseudo-rapidity |η|≤2.4. The results are unfolded from the detector level to the Born, bare and dressed levels, where a precision of < 0:6% is reported in the central bins. The data is combined with an electron channel measurement resulting in a combined result with reduced total uncertainty. The combined result is shown to constrain PDF uncertainties and achieve the most constrained sin²θeff./W uncertainty yet reported at the LHC.