Optimal progressive taxation in a model with endogenous skill supply

No abstract available

CP, T and CPT Violations in the K^0 - bar{K^0} System -- Present Status --

Possible violation of CP, T and CPT symmetries in the K^0 - bar{K^0} system is studied in a way as phenomenological and comprehensive as possible. For this purpose, we first introduce parameters which represent violation of these symmetries in mixing parameters and decay amplitudes in a convenient and well-defined way and, treating these parameters as small, derive formulas which relate them to the experimentally measured quantities. We then perform numerical analyses to derive constraints to these symmetry-violating parameters, with the latest data reported by KTeV Collaboration, NA48 Collaboration and CPLEAR Collaboration, along with those compiled by Particle Data Group, used as inputs. The result obtained by CPLEAR Collaboration from an unconstrained fit to a time-dependent leptonic asymmetry, aided by the Bell-Steinberger relation, enables us to determine or constrain most of the parameters separately. It is shown among the other things that (1) CP and T symmetries are violated definitively at least at the level of 10^{-4} in 2 pi decays, (2) CP and T symmetries are violated at least at the level of 10^{-3} in the K^0 - bar{K^0} mixing, and (3) CPT symmetry is at present tested to the level of 10^{-5} at the utmost.Comment: 20 page

Digital Resilience through Training Protocols: Identifying Fake News on Social Media

We explore whether training protocols can enhance the ability of social media users to detect fake news, by conducting an online experiment (N=417) to analyse the effect of such a training protocol, while considering the role of scepticism, age, and level of education. Our findings show a significant relationship between the training protocol and the ability of social media users to detect fake news, suggesting that the protocol can play a positive role in training social media users to recognize fake news. Moreover, we find a direct positive relationship between age and level of education on the one hand and ability to detect fake news on the other, which has implications for future research. We demonstrate the potential of training protocols in countering the effects of fake news, as a scalable solution that empowers users and addresses concerns about the time-consuming nature of fact-checking

Operationalizing fairness in medical AI adoption: Detection of early Alzheimer’s Disease with 2D CNN

Objectives: To operationalize fairness in the adoption of medical artificial intelligence (AI) algorithms in terms of access to computational resources, the proposed approach is based on a two-dimensional (2D) Convolutional Neural Networks (CNN), which provides a faster, cheaper, and accurate-enough detection of early Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI), without the need for use of large training datasets or costly high-performance computing (HPC) infrastructures. Methods: The standardized ADNI datasets are used for the proposed model, with additional skull stripping, using the BET2 approach. The 2D CNN architecture is based on LeNet-5, the LReLU activation function and a Sigmoid function were used, and batch normalization was added after every convolutional layer to stabilize the learning process. The model was optimized by manually tuning all its hyperparameters. Results: The model was evaluated in terms of accuracy, recall, precision, and f1-score. The results demonstrate that the model predicted MCI with an accuracy of .735, passing the random guessing baseline of .521, and predicted AD with an accuracy of .837, passing the random guessing baseline of .536. Discussion: The proposed approach can assist clinicians in the early diagnosis of AD and MCI, with high-enough accuracy, based on relatively smaller datasets, and without the need of HPC infrastructures. Such an approach can alleviate disparities and operationalize fairness in the adoption of medical algorithms. Conclusion: Medical AI algorithms should not be focused solely on accuracy but should also be evaluated with respect to how they might impact disparities and operationalize fairness in their adoption

Modelled testbeds: Visualizing and augmenting physical testbeds with virtual resources

Testbed facilities play a major role in the study and evolution of emerging technologies, such as those related to the Internet of Things. In this work we introduce the concept of modelled testbeds, which are 3D interactive representations of physical testbeds where the addition of virtual resources mimicking the physical ones is made possible thanks to back-end infrastructure. We present the architecture of the Syndesi testbed, deployed at the premises of University of Geneva, which was used for the prototype modelled testbed. We investigate several extrapolation techniques towards realistic value assignment for virtual sensor measurements. K-fold cross validation is performed in a dataset comprising of nearly 300’000 measurements of temperature, illuminance and humidity sensors collected from the physical sensors of the Syndesi testbed, in order to evaluate the accuracy of the methods. We obtain strong results including Mean Absolute Percentage Error (MAPE) levels below 7%

Optimal progressive taxation in a model with endogenous skill supply

No abstract available

A Federated Network Architecture Perspective on the Future of the Metaverse

We draw upon the concept of federated network architectures to better understand whether the Metaverse will develop into a ‘uni-verse’ or a ‘multi-verse’. We incorporate a historical research perspective enhanced with semi-structured interviews with experts, who are involved in developing and investing in the Metaverse. Our findings showcase that the Metaverse will develop into an expansive three-dimensional simulated reality, offering a large range of functionalities. Moreover, the insights of experts show that the Metaverse will most likely first develop into a ‘multi-verse’, through a collection of separate platforms each with its own function. The findings of our study, therefore, bear timely and topical insights for research as it is in line with the recent Information Systems research agenda and the industry. We discuss the implications of our study for both theory and practice, while we delineate an agenda for future research on the topic

Structure, classifcation, and conformal symmetry, of elementary particles over non-archimedean space-time

It is known that no length or time measurements are possible in sub-Planckian regions of spacetime. The Volovich hypothesis postulates that the micro-geometry of spacetime may therefore be assumed to be non-archimedean. In this letter, the consequences of this hypothesis for the structure, classification, and conformal symmetry of elementary particles, when spacetime is a flat space over a non-archimedean field such as the $p$-adic numbers, is explored. Both the Poincar\'e and Galilean groups are treated. The results are based on a new variant of the Mackey machine for projective unitary representations of semidirect product groups which are locally compact and second countable. Conformal spacetime is constructed over $p$-adic fields and the impossibility of conformal symmetry of massive and eventually massive particles is proved

From Groups to Communities: A Resource Mobilization Theory Perspective on the Emergence of Communities

Groups and communities have been key topics in the information systems (IS) research agenda. While communities are assumed to emerge at the intersection of overlapping groups and their practices, prior research has mainly focused on their dynamics and evolution. This has resulted to limited empirical support regarding the emergence of communities. We address that lacuna by tracing the emergence of communities through the prism of resource mobilization theory. In doing so, we make use of a unique longitudinal dataset and incorporate Topic Modelling, Bipartite Network Analysis, and Community Detection. We show that new communities are formed at the intersection of overlapping groups and practices. In addition, we contribute to the IS literature by demonstrating that their emergence occurs due to resource mobilization that gives rise to a shared mindset. We also reveal that multiple resources are incorporated into the practices of an emerging community. By combining large datasets and innovative computational approaches, we help IS theory and practice to move away from traditional "what" questions towards the more insightful "how" ones. We discuss the theoretical and practical implications of our work and delineate an agenda for future research on the topic