Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Foundations of Cryptoeconomic Systems

Blockchain networks and similar cryptoeconomic networks aresystems, specifically complex systems. They are adaptive networkswith multi-scale spatiotemporal dynamics. Individual actions towards a collective goal are incentivized with "purpose-driven" tokens. These tokens are equipped with cryptoeconomic mechanisms allowing a decentralized network to simultaneously maintain a universal state layer, support peer-to-peer settlement, andincentivize collective action. These networks therefore provide a mission-critical and safety-critical regulatory infrastructure for autonomous agents in untrusted economic networks. They also provide a rich, real-time data set reflecting all economic activities in their systems. Advances in data science and network sciencecan thus be leveraged to design and analyze these economic systems in a manner consistent with the best practices of modern systems engineering. Research that reflects all aspects of these socioeconomic networks needs (i) a complex systems approach, (ii) interdisciplinary research, and (iii) a combination of economic and engineering methods, here referred to as "economic systems engineering", for the regulation and control of these socio-economicsystems. This manuscript provides foundations for further research activities that build on these assumptions, including specific research questions and methodologies for future research in this field.Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Oral contraceptive use and salivary C-erbB-2, CEA and CA15-3 in healthy women : a case-control study

Objectives: Oral contraceptives (OCP) are highly effective, safe and widely used. Higher exposure to endogenous and exogenous estrogens is generally thought to increase the risk of breast cancer. Therefore, this study was conducted to determine if oral contraceptive use affected the expression of CA 15-3, CEA and C-erb B-2 in the saliva of healthy women. Study design: The participants consisted of 87 healthy women (43 controls and 44 using oral contraceptives) ranging in age from 20 to 54 years. The volunteers participated by giving one ? time stimulated whole saliva samples. Then the samples were analysed for CA 15-3, CEA and C-erb B-2 concentrations. Results: The student t-test was used to compare group means for variables with comparable variability. The mean of C-erb B-2, CEA, and CA 15-3 concentrations (in the case and control groups) was (1.93, 1.70), (34.46, 31.62) and (12.58, 16.19) respectively. These differences were not statistically significant. Conclusions: Our findings suggest that the levels of the cancer biomarkers C-erb B-2, CEA and CA 15-3 were not affected by increased levels of estrogens in the body

Stereospecific Ring Contraction of Bromocycloheptenes through Dyotropic Rearrangements via Nonclassical Carbocation-Anion Pairs

Experimental and theoretical evidence is reported for a rare type I dyotropic rearrangement involving a [1,2]-alkene shift, leading to the regio- and stereospecific ring contraction of bromocycloheptenes. This reaction occurs under mild conditions, with or without a Lewis acid catalyst. DFT calculations show that the reaction proceeds through a nonclassical carbocation-anion pair, which is crucial for the low activation barrier and enantiospecificity. The chiral cyclopropylcarbinyl cation may be a transition state or an intermediate, depending on the reaction conditions

Total synthesis of rubriflordilactone A

Rubriflordilactones A and B are highly oxygenated nortriterpenoid natural products isolated from Schisandra rubriflora. The latter is of particular biological interest as it shows significant anti-HIV activity. Two transition metal-catalysed cascade cyclisation approaches for the formation of the CDE rings of the rubriflordilactones were developed. Palladium-catalysed cyclisation of bromoenediynes and cobalt-catalysed triyne cyclotrimerisation both transform acyclic precursors into 7,6,5-bisannelated arenes in a single step. Two enantioselective syntheses of the AB ring fragment common to both rubriflordilactones, with bromoene or alkyne functional groups required for the respective cyclisation methods, are described; along with the refinement of a route to the CDE diyne fragment of rubriflordilactone A. From these fully functionalised bromoenediyne and triyne substrates, both metal-catalysed cyclisation methods were successful; these strategies converged on a late-stage intermediate bearing the ABCDE ring system of rubriflordilactone A. Construction of the F ring, followed by attachment of the G ring by an intriguing oxo-carbenium ion addition reaction completed two enantioselective total syntheses of (+)-rubriflordilactone A

Enhancing deep transfer learning for image classification

Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired from related domains to improve performance on a target domain. Existing transfer learning approaches lack proper high-level source domain feature analyses and are prone to negative transfers for not exploring proper discriminative information across domains. Current approaches also lack at discovering necessary visual-semantic linkage and has a bias towards the source domain. In this thesis, to address these issues and improve image classification performance, we make several contributions to three different deep transfer learning scenarios, i.e., the target domain has i) labelled data; no labelled data; and no visual data. Firstly, for improving inductive transfer learning for the first scenario, we analyse the importance of high-level deep features and propose utilising them in sequential transfer learning approaches and investigating the suitable conditions for optimal performance. Secondly, to improve image classification across different domains in an open set setting by reducing negative transfers (second scenario), we propose two novel architectures. The first model has an adaptive weighting module based on underlying domain distinctive information, and the second model has an information-theoretic weighting module to reduce negative transfers. Thirdly, to learn visual classifiers when no visual data is available (third scenario) and reduce source domain bias, we propose two novel models. One model has a new two-step dense attention mechanism to discover semantic attribute-guided local visual features and mutual learning loss. The other model utilises bidirectional mapping and adversarial supervision to learn the joint distribution of source-target domains simultaneously. We propose a new pointwise mutual information dependant loss in the first model and a distance-based loss in the second one for handling source domain bias. We perform extensive evaluations on benchmark datasets and demonstrate the proposed models outperform contemporary works.Doctor of Philosoph