Spillovers Between Russia’s and Turkey’s Geopolitical Risk During the 2000–2021 Putin Administration

This paper sets out to explore the nexus between Russia and Turkey regarding their geopolitical uncertainty measures (GPR) during the Putin Administration era in Russia. The innovative Caldara and Iacoviello indices and the Vector Autoregressive (VAR) methodology are adopted. This study sheds light on the series of geopolitical events that have taken place in Russia and Turkey in recent decades. Empirical outcomes reveal that Turkish geopolitical uncertainty is a weak influencer that increases Russian GPR in the short-term while decreasing it in the medium-term. The reverse effect does not hold. The nexus between geopolitical risk in Turkey and Russia is found to be unstable. Uncertainty in Turkey constitutes both a negative and a positive determinant of geopolitical stability in Russia, depending on the time horizon of the impact. Russia could take advantage of Turkish positive effects in the medium-run. This could be alarming for investors but could also prove beneficial as they should not invest in Russian assets when the country’s geopolitical risk is elevated due to Turkey’s geopolitical instability. Additionally, it is documented that energy financial markets in Russia are not influential on geopolitical uncertainty.

POLICY UNCERTAINTY AND THE DEMAND FOR MONEY IN SINGAPORE: AN ASYMMETRIC ANALYSIS

In search of a stable demand for money, almost all previous studies include two uncertainty measures captured by the volatility of the money supply and output. While in some countries, this yielded a stable demand for money, in some others, it did not. The latter was the case for Singapore. In this paper, we use a relatively more new and comprehensive measure of uncertainty known as policy uncertainty that is a news-based measure, and revisit the demand for money in Singapore. Our approach not only yields a stable demand for money in Singapore, but also reveals that the long-run effects of policy uncertainty on the demand for money are asymmetric. While increased uncertainty induces the public in Singapore to hold more money, decreased uncertainty does not affect.

High Dimensional Atomic States of Hydrogenic Type: Heisenberg-like and Entropic Uncertainty Measures

High dimensional atomic states play a relevant role in a broad range of quantum fields, ranging from atomic and molecular physics to quantum technologies. The D-dimensional hydrogenic system (i.e., a negatively-charged particle moving around a positively charged core under a Coulomb-like potential) is the main prototype of the physics of multidimensional quantum systems. In this work, we review the leading terms of the Heisenberg-like (radial expectation values) and entropy-like (Rényi, Shannon) uncertainty measures of this system at the limit of high D. They are given in a simple compact way in terms of the space dimensionality, the Coulomb strength and the state’s hyperquantum numbers. The associated multidimensional position–momentum uncertainty relations are also revised and compared with those of other relevant systems.

Exploring Uncertainty Measures for Image-caption Embedding-and-retrieval Task

With the significant development of black-box machine learning algorithms, particularly deep neural networks, the practical demand for reliability assessment is rapidly increasing. On the basis of the concept that “Bayesian deep learning knows what it does not know,” the uncertainty of deep neural network outputs has been investigated as a reliability measure for classification and regression tasks. By considering an embedding task as a regression task, several existing studies have quantified the uncertainty of embedded features and improved the retrieval performance of cutting-edge models by model averaging. However, in image-caption embedding-and-retrieval tasks, well-known samples are not always easy to retrieve. This study shows that the existing method has poor performance in reliability assessment and investigates another aspect of image-caption embedding-and-retrieval tasks. We propose posterior uncertainty by considering the retrieval task as a classification task, which can accurately assess the reliability of retrieval results. The consistent performance of the two uncertainty measures is observed with different datasets (MS-COCO and Flickr30k), different deep-learning architectures (dropout and batch normalization), and different similarity functions. To the best of our knowledge, this is the first study to perform a reliability assessment on image-caption embedding-and-retrieval tasks.