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Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. This necessitates the need for promoting sustainable computing approaches for achieving environment-friendly and energy-efficient datacentre execution. One of the direct causes of excess energy consumption of the datacentre includes running servers at a low level of workloads and over-provisioning of server resources to the arriving workloads during execution. Another cause of excess energy consumption of the datacentre is the lack of an efficient energy-aware workloads scheduling paradigm that is able to allocate optimal execution resources to various workloads. With this in mind, a novel energy-efficient Cloud resource management approach is proposed in this thesis, which is enabled by highly reliable workload prediction and scheduling models. Firstly, a classification-aware workloads forecasting model named K-RVLBPNN has been developed for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means and BPNN algorithms. This prediction model achieves reliable prediction accuracy at a high level. Furthermore, it is found stragglers within Cloud workload executions are obvious energy consumers and will heavily influence prediction accuracy. To improve the prediction accuracy, the IGRU-SD model is presented in this thesis, which is based on big data analytics and Recurrent Neural Networks. The proposed model exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Finally, to further improve the energy efficiency of Cloud datacentres, an energy-efficient Cloud-edge collaboration scheduling framework has been designed to reduce energy consumption while improving the efficiency of workload processing. The collaboration framework encompasses a task classification module to judge the type of incoming workload, a meta-heuristic Cloud scheduler (DSGA) for scheduling Cloud tasks, and a light-weighted edge scheduler (EA-DFPSO) for scheduling edge tasks. Extensive experiments demonstrate the effectiveness of the proposed approach in terms of the prediction results and energy saving for the Cloud datacentres.

Hands are some of the most complex and enabling instruments that humans use. Their dexterity, from their multiple degrees of freedom, has allowed people to create, communicate, and overall transform thoughts into actions that change the world. The advent of artificial sensing from cameras promises that the richness of data that hands can offer can be taken further if understood and processed. Being able to decode what hands do opens up multiple possibilities for human-computer interaction, especially when hands can be observed without intrusion. From computers and robotic devices learning how to perform actions, to translating explicit and implicit communication and also linking with Extended Reality applications (VR and AR), the decoding of hands is a key pursuit for understanding people and how we operate in the world. This thesis starts with hand feature extraction as a way to decode what hands are doing. To begin, we examine the hand's most critical feature: the hand pose, and we implement two approaches for extracting it: model-based and data-based. For the model-based approach, a kinematics-based hand skeleton is built to fit the observed depth data to achieve hand pose estimation. In the data-based approach, we implement a model based on a convolutional neural network to predict the position of the hand joints. To address the data scarcity issue, we created a synthetic dataset and a multi-camera data collection setup. The synthetic data can be given an exact annotation, and the amount of data and the way in which it is collected can be programmed. The multi-cam is built to compensate for the lack of realism in the synthetic data and to allow i data to be collected when the hand is obscured by an object. To further investigate the relationship between the hand and the objects in the environment, we attempted to extract additional features such as the hand mask and in-hand object mask during the hand-object interaction. The features obtained from the in-hand object interactions were then combined to determine the hand-object interaction's status. The thesis then looks into how the extracted hand features can be used in human-computer interactions. We examined two possible scenarios. The first scenario is to capture how humans interact with a machine they need to operate, so that the system can subsequently teach a novice without the use of paper instructions. We invited participants to help us evaluate our system. The results based on sewing machine operations show that our system is more efficient than the paper instruction for participants without any expertise. The second application scenario we demonstrate is how to segment a hand manipulation video automatically by extracting the features of both hands in a complex scene with various objects. We invited participants to conduct experiments on our own collected and labelled Chinese Tea Making dataset. The results showed that the automatic segmentation results were very close to expert's manual annotation results, and the participants were able to identify the steps in the task with watching the segmented video clips. Overall, we hope that the research presented in this thesis will inspire future work on hands and broaden the scope of uses for hands as a cue in intelligent systems.

Melodinus eugeniifolius. a very rare medicinal plant belongs to the family of Apocynaceae. This family consists of 424 genera and 1,500 species of trees. In the Asia-Pacific region, about 20 species of Apocynaceae are used to treat various diseases like cancer, cardiovascular diseases and infectious diseases. Furthermore, almost no pharmacological and phytochemical studies in genera Melodinus, no pharmacological and phytochemical studies in this plant as well. This study was undertaken to screen the phytochemical and phytochemical aspects on the plant Melodinus eugeniifolius. Qualitative phytochemical analysis of the crude extract was determined for the presence of tannins, flavonoids, alkaloids, saponins and sterols. Phytochemical analysis of Melodinus eugeniifolius. revealed presence of alkaloids, cardiac glycosides, sterols, steroids and flavonoids in crude extracts of leaves and barks. The crude extracts of the plant were subjected to several biological assays and were then focused on anticancer aspect. Bacterial infections have been known to generate extensive formation of free radicals which is becoming increasingly recognized in the pathogenesis of the many human diseases. Melodinus eugeniifolius leaves and barks were screened for the antibacterial activities, among the different extracts tested, all the extracts displayed remarkable antibacterial activity against Staphylococcus aureus, Bacillus cereus and Escherichia coli. The role of free radicals and active oxygen is becoming increasingly recognized in the pathogenesis of the many human diseases, including cancer, neurodegenerative diseases, ageing, and atherosclerosis. The FRAP assay (Ferric reducing antioxidant power), DPPH radical scavenging assay (1,1-diphenyl-2-picryl hydrazyl radical reducing power methods), and β-carotene bleaching assays were employed to determine the radical scavenging activity of the plant extracts using 96-well microtiter plate. Percentage decolourisation was plotted against the concentration of the sample and the EC50 values were determined. Vitamin C (l-ascorbic acid), Gallic Acid and Quercetin were used as positive control. Ethanol extracts showed significant antioxidant activity. In this test, the ethanol extract of leaves and barks exhibited profound antioxidant activities. The EC50 (μg/mL) for the ethanol extract of barks was as good as Gallic acid. Plant substances continue to serve as a wellspring of drugs for the world population and several plant-based drugs are in extensive clinical use. Preliminary antiparasitic screening of six extracts of Melodinus eugeniifolius leaves and barks were screened for their in vitro antileishmanial and antitumor activities, among the different extracts tested, the ethanol and hexane extract of barks showed significant antileishmanial activities. Cancers remain the leading cause of death worldwide and the search for novel anti-cancer agents from medicinal plants has become crucial. Infections and free radical generation are recognized in the pathogenesis of cancer. Six different extractions of Melodinus eugeniifolius leaves and barks were screened for their in vitro antitumor activities, among the different extracts tested, the chloroform extracts of barks and leaves showed significant antitumor activities and would not damage the normal cells. According to the current result, susceptible cell lines were found to be the HT-29 human colon cancer cell line, HCT-116 human colon cancer cell line, MCF-7 human breast cancer cell line, Caco-2 human colon adenocarcinoma cell line, HK-1 human nasopharyngeal cancer cell line, and MRC-5 human lung fibroblast cell line. Results from our phytochemical analysis revealed that the chloroform extract of leaves and barks of Melodinus eugeniifolius accumulate substantial amounts of alkaloids and cardiac glycosides which could be well correlated with the activities measured. Bioassay guided isolation and nine chemical compounds were isolated by column chromatography fractions and were identified by NMR techniques. Five indole alkaloids were isolated and were identified as yohimbine hydrochloride, tabersonine, yohimbine, β-yohimbine, and leuconolam respectively. The new alkaloid, having a pentacyclic diazaspiro system, was named Melodinoid. Other constituents identified were epigallocatechin and loganic acid. Some of the compounds isolated from the plant were subjected to biological assays and were then focused on anti-cancer aspect. Alkaloids always present significant anticancer capacity. Three alkaloids isolated from Melodinus eugeniifolius were screened by MTT assays against two cancer cells and a normal cell. All the compounds revealed significant anticancer capacity on the two human cancer cell lines, and not damage the normal cells. Hence, further research on the mechanism of some of the isolated compounds with target pharmacological activities, relative in vivo anticancer tests, and even clinical trials are recommended to ascertain the efficacy, safety and other mechanisms of action to application in the pharmaceutical industry as natural therapeutic agents.

Reinsurance contract as a contract of uberrimae fidei, in contrast to ordinary commercial contracts, attracts a duty of utmost good faith requiring both parties to exercise their best effort and endeavor to help each other to make an informed decision and perform the contract concluded thereon without any dishonesty or deceit. There are various forms of reinsurance which adopt different ceding methods and have specific characters in the placing progress. The unique placing process in London subscription market of such complex and complicated reinsurance contracts by specialist brokers has to certain degree modified the operation of the doctrine of utmost good faith in reinsurance context. Moreover, from partial codification by the MIA 1906 to significant changed by Insurance Act 2015, it is fair to that the doctrine of utmost good faith has experienced one hundred years long revolution. The courts have taken many opportunities to structure the doctrine, establish rules of the tests, confine the scope and clarify remedies for qualifying breach. Such development of the doctrine itself has important affect upon its operation in reinsurance context too. Modification of the doctrine in reinsurance occurs due to several reasons. First, the special placing process in London subscription market affects the formation procedure of reinsurance contracts, consequently reshapes operation of the doctrine. Secondly, the characters of reinsurance contracts distinguished from underlying insurance would have some impact on operation of the doctrine in reinsurance context. In addition, other significant common law rules such as the principle of waiver, which is in extensive use in the reinsurance market practice, will also modify the operation of the doctrine in reinsurance context. Moreover, evolution of the duty itself, from an absolutely strict duty to a duty only requiring fair presentation, and a proposal of a new proportionate regime of remedies brings potential problems of its operation in reinsurance context. Consequently, notwithstanding there has been a long history of the doctrine and clarification of many aspects of the doctrine comes from a reinsurance cases, difficulties and problems still exist in operating such duty smoothly and directly in reinsurance like in direct insurance context. Such problems extend to every specific aspect of operation of the duty in reinsurance context, from the formation to performance, and then remedies for qualifying breach of the duty in claim stage.

I study how liquidity information influences banks' liquidity holdings, using the disclosure of bank liquidity coverage ratio (LCR) mandated for a group of large US banks. While the disclosure rule aims to increase liquidity in the banking system, I find that non-disclosing banks responded by reducing liquid asset holdings due to the impact of liquidity information on banks' strategic interactions in holding liquidity. I use bank network relationships to measure how much a bank learns from the disclosure, and find that banks learning more cut their liquidity significantly more. In the aggregate, the new disclosure rule lowered liquidity in the banking system, concentrated liquidity within a group of large disclosing banks, and ultimately increased systemic risk. My findings highlight the important, and potentially unanticipated, influence of liquidity information on the liquidity and stability of the banking system.

Quantum computing is at an exciting time, with practical quantum processors coming closer to experimental realization. Yet a major challenge in building a practical quantum computer is to generate and manipulate interactions between its many components. Superconducting (SC) qubits are promising candidates not only because they have strong coupling and high-fidelity readout, but also for allowing versatile parametric control that can realize different effective interactions at will, which might be difficult to achieve through other means. In this thesis, we start from the simple example of parametrically flux modulated SC qubit and demonstrate its application towards quantum communication between remote SC modules. We then move on to discuss the parametric modulation of the light-matter interaction strength, where we introduce a novel SC tunable coupler device that allows for the direct dc-flux control of qubit-qubit or qubit-cavity coupling strength without sacrificing qubit coherence, as well as the convenient realization of blue- and red-sideband interactions through appropriate choice of parametric flux-modulation frequency. By engineering the dissipative system-environment interactions through sophisticated parametric control of this tunable coupler device, we achieve the autonomous stabilization of arbitrary qubit states, in a manner akin to laser cooling in atomic physics. Finally, we present our on-going experimental effort of extending the idea of autonomous stabilization to autonomous quantum error correction, an important step towards the ultimate realization of universal quantum computer.

Cloud gaming architecture has been proposed and deployed in large-scale com- mercial systems in recent years. It renders and encodes video views on cloud servers, with the resulting video streamed through network to end devices. This approach has the advantage of relieving high computation, power and storage requirements of gaming from end devices. The challenge therefore shifts to streaming high quality video with low latency through fluctuating network. In this thesis, we extend the basic cloud gaming architecture in three aspects. First, we consider not only gaming as target application, but also consider much more virtual immersive applications, such as virtual classroom and virtual art gallery, etc. Second, with the development of ubiquitous wireless network for mobile devices, we specifically consider streaming the video through mobile wireless network. Third, with the growing popularity of mobile auto-stereoscopic 3D displays, we consider capture, record and stream two views (left and right views) in the virtual world so that it can render 3D views on mobile 3D displays. To conclude, we term our extended architecture as cloud mobile 3D display virtual immersive application architecture. Because 3D videos contain two views that doubles video bit rate requirement for the same quality versus 2D video, and also because mobile wireless network is much more fluctuating than wired and WiFi network, streaming high quality 3D video with low latency becomes much more challenging. In this thesis, based on prior research results that show human brain can compensate perceived video quality automatically when one of the views is of inferior quality, we propose asymmetric rendering where we tune graphics rendering quality to be asymmetric in rendering engine. We develop both user experience models and bit rate models by applying different rendering settings and conducting subjective tests. We further develop optimization algorithms which use the above two models to automatically decide the optimal rendering settings for left view and right view to ensure the best user experience given the network conditions. Experiments conducted using real 4G-LTE network profiles on commercial cloud service with different genres of applications demonstrate significant improvement in user experience when the proposed technology is applied.

Rapid, practical, and low-cost analytical methods are always desirable in forensic analysis. Using proper sample preparation techniques with the application of gas chromatography/mass spectrometry (GC/MS), gas chromatography-differential mobility spectrometry (GC-DMS) and ion mobility spectrometry (IMS) with chemometric analysis, analytical methods were developed for fast and practical identification and classification of analytes in complicated matrices.GC-DMS was investigated as a tool for analysis of ignitable liquids from fire debris. The combined information afforded by gas chromatography and differential mobility spectrometry provided unique two-way patterns for each sample of ignitable liquid. Fuzzy rule-building expert system (FuRES) models constructed with the neat ignitable liquids identified the spiked samples from simulated fire debris with 99.07±0.04% accuracy. The performances of DMS as gas chromatographic detector was also compared with mass spectrometry (MS) using a chemometric tool, projected difference resolutions (PDRs). The PDR results show that one-way mass spectra data exhibit higher resolution than DMS data, while total ion chromatograms from GC-DMS show higher resolution than that from GC/MS for differentiating seven kinds of ignitable liquids.Direct methylation and solid phase microextraction (SPME) were used as a sample preparation technique for classification of bacteria based on fatty acid methyl esters (FAMEs) profiles. Compared with traditional chemical derivatization and liquid-liquid extraction (LLE), the method presented in this work avoids using inorganic and organic solvents and greatly decrease sample preparation time as well. The difference between Gram-positive and Gram-negative bacteria was clearly observed with the application of principal component analysis (PCA) of GC/MS data of bacterial FAMEs. The cross-validation study using ten bootstrap Latin partition (BLP) and fuzzy rule building expert system (FuRES) presented an 87±3% correct classification rate.A comparatively rapid and reliable screening method for detection of cocaine and its metabolites, benzoylecgonine and cocaethylene in urine was demonstrated using solid phase extraction (SPE) coupled with IMS. Data analysis with alternating least squares (ALS) was used to model the IMS spectral datasets and separate the reactant ion peak from the product ion peaks. This method provides forensic chemists a viable approach for fast and simple drug screening.

My dissertation studies the issues on corporate governance in a global context. In the first chapter, I study a well-documented but little understood phenomenon that foreign acquirers target only well-performing in emerging markets. To explain this phenomenon, I develop a law and finance model, in which I argue that controlling shareholders of acquiring firms from strong-investor protection (IP) countries consume fewer private benefits and, hence, value control premia lower than does a controlling shareholder in a weak-IP country. Within a weak-IP country, controlling shareholders of well-performing firms have less incentive to consume private benefits because of greater opportunity costs of foregoing profitable investment opportunities. Thus, these firms demand lower control premia, making them more palatable to a foreign acquirer from a strong-IP country. These hypotheses are supported by data on cross-border acquisition bids. They reveal that foreign acquirers target only well-performing firms in weak-IP countries, but not in strong-IP countries. I also conduct a difference-in-difference estimation using corporate governance reforms (CGRs) undertaken by 29 countries since 1999. I find that the cherry picking tendency declines when target countries undertake CGRs, narrowing the gap in IP between acquirer and target countries. Conversely, the cherry picking tendency increases when acquirers’ home countries undertake CGRs, enlarging the IP gap. The second chapter is based on a paper co-authored with E. Han Kim. We find a hump shaped relation between Tobin’s Q and CEO share ownership, but only when external pressure for good governance is weak, where the pressure is measured by product market competition and institutional ownership concentration. When external governance is strong, CEO share ownership is unrelated to Tobin’s Q. These results are robust to firm or CEO-firm pair fixed effects, alternative definitions of key variables, different statistical properties between strong and weak external governance regimes, founder effects, reverse causality, and other endogeneity issues. The hump shaped relation appears to be a manifestation of some CEOs capturing incentive contracts under weak external governance, while no relation under strong external governance is consistent with the contracting view that CEO ownership is a component of equilibrium contracts.