セルラーネットワークにおける無線LANオフロードのためのネットワーク選択方式に関する研究

早大学位記番号:新7456早稲田大

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page

Essays on Measuring Credit and Property Prices Gaps

My dissertation studies credit expansion and its effect on house prices and financial stability. In the first chapter, I examine the idea that house prices and credit to households are jointly determined, affecting each other in the short and long run. I decompose the movements of the two variables of interest into permanent long-run and transitory short-run components using an unobserved components vector autoregressive model. The dynamic model shows findings to support the hypothesis that a short-run positive shock to house prices is associated with an increase in household credit above its long-run trend. Furthermore, by utilizing additional information generated by the unobserved component model, our multivariate model performs better than univariate models in capturing the dynamics of household credit and house prices over the last three decades, especially during the recent financial crisis. I also estimated the predictive ability of cyclical components of a variable on their counterparts from other variables by employing cross-correlation coefficients in the VAR model. In the second chapter, I propose a new method to measure the credit gap - the deviation of the credit-to-GDP ratio from its long-run trend. Here, I utilize the idea proposed in Nelson (2008) that the deviation of a non-stationary variable from its long-run trend should predict future changes in the variable. Since different trend-cycle decomposition methods of credit-to-GDP ratio provide different credit gap measures, I handle the model uncertainty by assigning weights to these different credit gap measures based on their relative out-of-sample predictive power based on Bates and Granger (1969) forecast combination method. I apply this approach to estimate the UK and the US credit gap using credit-to-GDP ratio data from 1960-2020. My proposed credit gap measure dominates the alternate credit gaps, including the one provided by the Bank of International Settlements (BIS) regarding its relative out-of-sample predictive power. The proposed gap also has superior features in terms of early detection of turning points and relative insensitivity to the endpoint problem. The third chapter of my dissertation overcomes model uncertainty in using the credit gap as an early warning indicator (EWI) of systemic financial crises in a binary outcome setting. I propose using model averaging of different credit gap measurements to achieve a better averaged model fit and out-of-sample prediction. I use binary logistic regression in a panel setup consisting of 40 countries. In this paper, I also propose a novel, superior criteria to judge the performance of an EWI than the one currently popularly used in the literature. The empirical results showed that our Bayesian averaged model could synthesize a single credit gap that outperforms other popularly studied credit gap measurements in terms of an early warning indicator

An effective theory for higher-dimensional black holes and applications to metastable antibranes

Despite their consequential applications across the subfields of high energy physics, metastable states of antibranes in warped throats are not yet fully understood. In this thesis, we provide new information on various aspects of these metastable antibranes through applications of the blackfold effective theory for higher-dimensional black holes. As concrete examples, we study the conjectured metastable state of polarised anti-D3 branes (namely, wrapped NS5 branes with dissolved D3 brane charge) at the tip of the Klebanov-Strassler (KS) throat in type IIB supergravity and the analogous state of polarised anti-M2 branes (namely, wrapped M5 branes with dissolved M2 brane charge) at the tip of the Cvetic-Gibbons-Lu-Pope (CGLP) throat in eleven-dimensional supergravity. For anti-D3 branes in KS throat, from a finite-temperature analysis in the wrapped NS5 regime, we provide novel evidence for the existence of the metastable state exactly where no-go theorems are lifted. In particular, in the extremal limit, we recover directly in supergravity the metastable states originally discovered by Kachru, Pearson, and Verlinde (KPV). Away from extremality, we uncover a metastable wrapped black NS5 state (the thermalised version of the KPV state) and observe that such metastability is lost when we heat the wrapped NS5 state sufficiently that its horizon geometry resembles that of a black anti-D3 state. All claims regarding metastability of antibranes in warped throats only refer to a balance of force and not statements of robustness under perturbations. For their various applications, it is important to determine whether these configurations are truly metastable by probing them with perturbations. Here, we study the classical stability of the KPV state under generic long-wavelength deformations. We observe that, with regards to considered perturbations and regime of parameters, the state is classically stable. A study of anti-M2 branes in CGLP throat reveals many similarities to that of the anti-D3 branes. We recover directly in supergravity the Klebanov-Pufu (KP) state at extremality, and our finite temperature results fit suggestively well with known, complementary no-go theorems. However, a unique feature of the anti-M2 state is that when considering the effects of non-zero temperature on the KP metastable state, we discover an exotic pattern of thermal transitions different from that of the KPV. This thesis contains detailed discussions on all the above results as well as a pedagogical introduction to the blackfold formalism, focusing on aspects immediately relevant to applications to metastable antibranes

Sparse image super-resolution via superset selection and pruning

This note extends the superset method for sparse signal recovery from bandlimited measurements to the two-dimensional case. The algorithm leverages translation-invariance of the Fourier basis functions by constructing a Hankel tensor, and identifying the signal subspace from its range space. In the noisy case, this method determines a superset which then needs to undergo pruning. The method displays reasonable robustness to noise, and unlike ℓ [subscript 1] minimization, always succeeds in the noiseless case.United States. Air Force Office of Scientific ResearchTOTAL (Firm)Alfred P. Sloan FoundationNational Science Foundation (U.S.)United States. Office of Naval Researc

Stability analysis of recurrent neural networks using dissipativity

The purpose of this work is to describe how dissipativity theory can be used for the stability analysis of discrete-time recurrent neural networks and to propose a training algorithm for producing stable networks. Using dissipativity theory, we have found conditions for the globally asymptotic stability of equilibrium points of Layered Digital Dynamic Networks (LDDNs), a very general class of recurrent neural networks. The LDDNs are transformed into a standard interconnected system structure, and a fundamental theorem describing the stability of interconnected dissipative systems is applied. The theorem leads to several new sufficient conditions for the stability of equilibrium points for LDDNs. These conditions are demonstrated on several test problems and compared to previously proposed stability conditions. From these novel stability criteria, we propose a new algorithm to train stable recurrent neural networks. The standard mean square error performance index is modified to include stability criteria. This requires computation of the derivative of the maximum eigenvalue of a matrix with respect to neural network weights. The new training algorithm is tested on two examples of neural network-based model reference control systems, including a magnetic levitation system

A Trade-Based Analysis of the Economic Impact of Non-Compliance with Illegal, Unreported and Unregulated Fishing: The Case of Vietnam

Illegal, unreported and unregulated (IUU) fishing is a threat to the sustainable use of fishing resources. To eliminate the destructive fishing practices, the whole value chain of fish trade needs to be well regulated. Trade-related policy measures show potential for contributing towards the elimination of unsustainable fishing practices. The EU’s launch of the IUU-combating fishing program and the introduction of measures to deal with countries that exploit, produce and export fishery products with illegal fishing origin, is indispensable in addressing harmful trends and a concern of the whole world, especially the fishing community. The program includes the flagship use of a warning card system. The EU is a very important trading partner for Vietnam and major importer of Vietnam’s fish products, of which seafood plays an important role. The EU market helps pave the way for Vietnamese seafood to enter the world market. Vietnam’s seafood export to the EU has increased sharply over the past 20 years, from USD 90 million in 1999 to nearly USD 1.5 billion in 2017 (and since decreased to closer to USD 1.3 billion in 2019). The year of 2017 marked a critical turning point for Vietnam’s fisheries when the EU issued a yellow card warning to Vietnam for not cooperating and making enough efforts to combat IUU fishing. The EU made nine recommendations to improve the Vietnamese fisheries management system following the warning. Over the past two years, the Government of Vietnam, ministries and the entire Vietnamese fishing community have actively improved to meet the recommendations of the EU to remove the IUU yellow card. The EU has appreciated Vietnam’s efforts to combat IUU exploitation, however, so far, the IUU yellow card has not yet been removed. In the past two years, the quantity of seafood exports to the EU have decreased significantly, showing the immediate impact of the yellow card warning on Vietnam’s seafood industry. However, that is only part of the negative impact as visible in export figures. There will be many other consequences from the IUU yellow card warning and the impact will be more serious if Vietnam does not remove the yellow card soon or receives a red card warning

The Impact of Foreign Direct Investment on Economic Growth: Evidence from Vietnam

The relationship between Foreign Direct Investment (FDI) and economic growth has long been a topic of great interest in the field of international development. Although this interest has provided rich insights into the impact of FDI on growth in developing countries, there are very few empirical analyses of the linkage in Vietnam compared to other developing countries. Therefore, this study investigates the impact of FDI inflows on economic growth in Vietnam over the period from 1990 to 2013 using time series analysis techniques that address the problem of nonstationarity. Specifically, the Unit root test and Cointegration approach are applied to ensure that the regressions are not spurious. The empirical results reveal that FDI inflows, domestic investment, trade openness and secondary education have positive impacts on economic growth whereas inflation rate is found to have negative effect on economic growth. In addition, the impact of government consumption on economic growth is negative and statistically insignificant. Ultimately, this paper suggests that Vietnamese government should improve regulations governing business activities by easing the process of business start-up, controlling price, enhancing public spending on education and training, and augmenting cooperation between training centers and Foreign-invested enterprises. Keywords: Foreign Direct Investment, economic growth, time series, unit root test, cointegration, Vietnam

Enhanced Performance in Polymer Light Emmiting Diode by Using Ultra-thin Conductive Films as the Buffer Layer

The ultra-thin nanocomposite films based on the nano-crystal TiO$_{2}$ (TiO$_{2}$-nc) or multi-walled carbon nano-tube (MWCNTs) were prepared and used as the buffer layers in the fabrication of the organic light-emitting diodes (OLEDs). The injection efficiency of the hole and electron was improved by inserting an ultra-thin buffer layer between the electrodes and emissive layer. The turn-on voltage of the device with the buffer layers was lowered from 4 to 2.5V, and the current density was increased from 0.3 to 0.7~mA/mm$^{2}$, in comparison with the device without such a buffer layer. These devices showed a high efficiency and good stability