As a degree project in Theoretical Physics, the variational MCMC-scheme aided by neural network quantum states was examined for the purpose ofsolving the nuclear pairing model. The method entailed minimization of the local energy sampled via the Born distribution obtained through the neural network output.Both the ground and excited states' energies were computed, where the latter case used an extended loss function which included the overlap to the former.The NNQS-ansatz worked well when emulating the ground state, in which case the Stochastic Reconfiguration optimization method was particularly effective. This optimization method resulted in relative fast convergence to low variance states, and did not require a large number of hyperparameter modifications. Ultimately, all resulting energy intervals encompassed the exact ground state solutions, and had relative errors equal to or near zero.For the excited states, the VMC-NNQS was less effective, as each individual occupation number state investigated required considerable hyperparameter testing before reasonably low lying energy eigenstates could be obtained. Moreover, the convergence properties were less distinguished than for the ground state, as the optimization struggled to maintain orthogonality to the ground state. Nonetheless, the final results included the nearest solutions of the first excited states for several systems and indicated correlation energies similar to those of the ground state. / Som examensarbete inom teoretisk fysik undersöktes den variationella MCMC-metoden tillsammans med neurala nätverk i syfte att lösa kärnparsmodellen. Metoden innebar minimering av den lokala energin som samplades via Born-fördelningen erhållen genom utdata från neurala nätverksapproximationer. Både grundtillståndets och exciterade tillstånds energier beräknades, där det senare fallet använde en utökad kostnadsfunktion som inkluderade överlappet med det förnämnda. NNQS-ansatsen fungerade väl vid emulering av grundtillståndet, i vilket fall optimeringsmethoden stokastisk omkonfigurering (Stochastic Reconfiguration) var särskilt effektivt. Denna optimeringsmetod resulterade i relativt snabb konvergens till lågvarianstillstånd och krävde inte ett stort antal hyperparametriska modifieringar. De slutliga energiintervallen innefattade de exakta lösningarna för grundtillstånden med en relativ felmarginal lika med eller nära noll. För exciterade tillstånd var VMC-NNQS mindre effektivt, eftersom varje enskilt ockupationstillstånd som undersöktes krävde en ansenlig mängd hyperparametrisk testning innan rimligt låga egentillstånd kunde erhållas. Dessutom var konvergensensegenskaperna mycket mindre särspäglade än för grundtillståndet, eftersom optimeringen inte fullt kunde upprätthålla ortogonaliteten mot grundtillståndet. Likväl inkluderade de slutliga resultaten de närmaste lösningarna av de första exciterade energierna för ett flertal system, och visade på korrelationsenergier liknande de för grundtillståndet.

Switching mode DC/DC converters are critical building blocks in portable devices and hence their power efficiency, accuracy and cost are a major issue. The primary focus of this thesis is to address these critical issues.This thesis focuses on the different methods of feedback control loop which are employed in the switching mode DC/DC converters such as voltage mode control and current mode control. It also discusses about the structure of buck converter and tries to find an efficient solution for stepping-down the DC voltage level in ultra-low power applications. Based on this analysis, a 20 MHz voltage mode DC/DC buck converter with an on-chip compensated error amplifier in 65 nm CMOS process is designed and implemented.The power efficiency has been improved by sizing the power switches to have a low parasitic output and gate capacitances to reduce the capacitive and gate-drive losses. Also the error amplifier biasing current is chosen a small value (12.5 μA) to reduce the power dissipations in the control loop of the system. The maximum 84% power efficiency is achieved at 1.1 V to 500 mV conversion, above 81% efficiency can be achieved at load current from 0.5 mA to 1.26 mA. Due to wide bandwidth error amplifier and proper compensation network the fast transient response with settling time around 45 μs is achieved.

This thesis is devoted to a study of the effect of disorder on strongly correlated electrons. For non-interacting electrons, Anderson localization occurs if the amount of disorder is sufficient. For disorder-free systems, a Mott metal-insulator transition may occur if the electron-electron interactions are strong enough. The question we ask in this thesis is what happens when both disorder and interactions are present. We study the Anderson-Hubbard model, which is the simplest model to include both interactions and disorder, using a Gutzwiller variational wave function approach. We then study Anderson localization of electrons from the response of the Anderson-Hubbard Hamiltonian to an external magnetic field. An Aharonov-Bohm flux induces a persistent current in mesoscopic rings. Strong interactions result in two competing tendencies: they tend to suppress the current because of strong correlations, and they also screen the disorder potential and making the system more homogenous. We find that, for strongly interacting electrons, the localization length may be large, even though the current is suppressed by strong correlations. This unexpected result highlights how strongly correlated materials can be quiet di erent from weakly correlated ones. / Thesis (Ph.D, Physics, Engineering Physics and Astronomy) -- Queen's University, 2011-08-31 09:51:47.155

Context: In recent years, Cloud computing has gained a wide range of attention in both industry and academics as Cloud services offer pay-per-use model, due to increase in need of factors like reliability and computing results with immense growth in Cloud-based companies along with a continuous expansion of their scale. However, the rise in Cloud computing users can cause a negative impact on energy consumption in the Cloud data centers as they consume huge amount of overall energy. In order to minimize the energy consumption in virtual datacenters, researchers proposed various energy efficient resources management strategies. Virtual Machine dynamic Consolidation is one of the prominent technique and an active research area in recent time, used to improve resource utilization and minimize the electric power consumption of a data center. This technique monitors the data centers utilization, identify overloaded, and underloaded hosts then migrate few/all Virtual Machines (VMs) to other suitable hosts using Virtual Machine selection and Virtual Machine placement, and switch underloaded hosts to sleep mode.   Objectives: Objective of this study is to define and implement new energy-aware heuristic algorithms to save energy consumption in Cloud data centers and show the best-resulted algorithm then compare performances of proposed heuristic algorithms with old heuristics.   Methods: Initially, a literature review is conducted to identify and obtain knowledge about the adaptive heuristic algorithms proposed previously for energy-aware VM Consolidation, and find the metrics to measure the performance of heuristic algorithms. Based on this knowledge, for our thesis we have proposed 32 combinations of novel adaptive heuristics for host overload detection (8) and VM selection algorithms (4), one host underload detection and two adaptive heuristic for VM placement algorithms which helps in minimizing both energy consumption and reducing overall Service Level Agreement (SLA) violation of Cloud data center. Further, an experiment is conducted to measure the performances of all proposed heuristic algorithms. We have used the CloudSim simulation toolkit for the modeling, simulation, and implementation of proposed heuristics. We have evaluated the proposed algorithms using PlanetLab VMs real workload traces.   Results: The results were measured using metrics energy consumption of data center (power model), Performance Degradation due to Migration (PDM), Service Level Agreement violation Time per Active Host (SLATAH), Service Level Agreement Violation (SLAV = PDM . SLATAH) and, Energy consumption and Service level agreement Violation (ESV).  Here for all four categories of VM Consolidation, we have compared the performances of proposed heuristics with each other and presented the best heuristic algorithm proposed in each category. We have also compared the performances of proposed heuristic algorithms with existing heuristics which are identified in the literature and presented the number of newly proposed algorithms work efficiently than existing algorithms. This comparative analysis is done using T-test and Cohen's d effect size.   From the comparison results of all proposed algorithms, we have concluded that Mean absolute Deviation around median (MADmedain) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified First Fit Decreasing VM placement (MFFD), and Standard Deviation (STD) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified Last Fit decreasing VM placement (MLFD) respectively performed better than other 31 combinations of proposed overload detection and VM selection heuristic algorithms, with regards to Energy consumption and Service level agreement Violation (ESV). However, from the comparative study between existing and proposed algorithms, 23 and 21 combinations of proposed host overload detection and VM selection algorithms using MFFD and MLFD VM placements respectively performed efficiently compared to existing (baseline) heuristic algorithms considered for this study.   Conclusions: This thesis presents novel proposed heuristic algorithms that are useful for minimization of both energy consumption and Service Level Agreement Violation in virtual datacenters. It presents new 23 combinations of proposed host overloading detection and VM selection algorithms using MFFD VM placement and 21 combinations of proposed host overloading detection and VM selection algorithms using MLFD VM placement, which consumes the minimum amount of energy with minimal SLA violation compared to the existing algorithms. It gives scope for future researchers related to improving resource utilization and minimizing the electric power consumption of a data center. This study can be extended in further by implementing the work on other Cloud software platforms and developing much more efficient algorithms for all four categories of VM consolidation.