EVOLUTION OF DOLLAR/EURO EXCHANGE RATE BEFORE AND AFTER THE BIRTH OF EURO AND POLICY IMPLICATIONS

One possible consequence of the establishment of the Euro is a challenge to the hegemony of the US dollar as the predominant international currency. No other currency has been able to rival the international role of the national currency of the US since World War II. The fact that the unipolar international monetary system can be unstable in the presence of large shocks opens a window of opportunity for the Euro to promote systemic stability. The present study pursues this conjecture by, first, exploring with cointegration and ECM techniques the interdependence between the dynamics of the Dollar/Euro exchange rate and economic fundamentals in the context of a monetary exchange rate model. Identification of the key determinants of the value of the Euro informs our analysis of the policy stance of the European Central Bank regarding the long-run global role of the Euro. Secondly, we explore whether the opportunity for a prominent systemic role of the Euro has been realized by examining the impact of the Euro on the global financial market.Euro, Exchange rate, Monetary model, Cointegration

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes

An adaptive dynamical low-rank tensor approximation scheme for fast circuit simulation

Tensors, as higher order generalization of matrices, have received growing attention due to their readiness in representing multidimensional data intrinsic to numerous engineering problems. This paper develops an efficient and accurate dynamical update algorithm for the low-rank mode factors. By means of tangent space projection onto the low-rank tensor manifold, the repeated computation of a full tensor Tucker decomposition is replaced with a much simpler solution of nonlinear differential equations governing the tensor mode factors. A worked-out numerical example demonstrates the excellent efficiency and scalability of the proposed dynamical approximation scheme.postprin

Fully nonlinear excitations of non-Abelian plasma

We investigate fully nonlinear, non-Abelian excitations of quark-antiquark plasma, using relativistic fluid theory in cold plasma approximation. There are mainly three important nonlinearities, coming from various sources such as non-Abelian interactions of Yang-Mills (YM) fields, Wong's color dynamics and plasma nonlinearity, in our model. By neglecting nonlinearities due to plasma and color dynamics we get back the earlier results of Blaizot {\it et. al.}, Phys. Rev. Lett. 72, 3317 (1994). Similarly, by neglecting YM fields nonlinearity and plasma nonlinearity, it reduces to the model of Gupta {\it et. al.}, Phys. Lett. B498, 223 (2005). Thus we have the most general non-Abelian mode of quark-gluon plasma (QGP). Further, our model resembles the problem of propagation of laser beam through relativistic plasma, Physica 9D, 96 (1983). in the absence of all non-Abelian interactions.Comment: 8 pages, 2 figures, articl

Determination of the High Prevalence of Dual/Mixed- or X4-Tropism Among HIV Type 1 CRF01_AE in Hong Kong by Genotyping and Phenotyping Methods

published_or_final_versio

Multiband gravitational-wave event rates and stellar physics

Joint gravitational-wave detections of stellar-mass black-hole binaries by ground- and space-based observatories will provide unprecedented opportunities for fundamental physics and astronomy. We present a semianalytic method to estimate multiband event rates by combining selection effects of ground-based interferometers (like LIGO/Virgo) and space missions (like LISA). We forecast the expected number of multiband detections first by using information from current LIGO/Virgo data, and then through population synthesis simulations of binary stars. We estimate that few to tens of LISA detections can be used to predict mergers detectable on the ground. Conversely, hundreds of events could potentially be extracted from the LISA data stream using prior information from ground detections. In general, the merger signal of binaries observable by LISA is strong enough to be unambiguously identified by both current and future ground-based detectors. Therefore third-generation detectors will not increase the number of multiband detections compared to LIGO/Virgo. We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections.Comment: 17 pages, 11 figures. Database and python code available at https://github.com/dgerosa/spops - Published in PR

Low-Complexity Precoding Design for Massive Multiuser MIMO Systems Using Approximate Message Passing

A practical challenge in the precoding design of massive multiuser multiple-input multiple-output (MIMO) systems is to facilitate hardware-friendly implementation. To achieve this, we propose a low peak-to-average power ratio (PAPR) precoding based on an approximate message passing (AMP) algorithm to minimize multiuser interference (MUI) in massive multiuser MIMO systems. The proposed approach exhibits fast convergence and low complexity characteristics. Compared with a conventional constant-envelope precoding and an annulus-constrained precoding, simulation results demonstrate that the proposed AMP precoding is superior both in terms of computational complexity and average running time. In addition, the proposed AMP precoding exhibits a much desirable tradeoff between MUI suppression and PAPR reduction. These findings indicate that the proposed AMP precoding is a suitable candidate for hardware implementation, which is very appealing for massive MIMO systems

INVISQUE: Technology and methodologies for interactive information visualization and analytics in large library collections

When a user knows exactly what they are looking for most library systems are adequate for their needs. However, when the user’s information needs are ill-defined - traditional library systems prove inadequate. This is because traditional library systems are not designed to support sense making rather for information retrieval. Visual analytics is the science of analytical reasoning facilitated by interactive visualizations and visual analytics systems can support both sense making and information retrieval. In this paper, we present INVISQUE - an approach and experimental software for interactive visual search and query. INVISQUE uses an index card metaphor to display library content, organized in a way that visually integrates attributes such citations and date published, making it easy to pick out the most recent and most cited paper. It uses design techniques such as focus+context to reveal relationships between documents, while avoiding the “what-was-I-looking-for?” problem

INVISQUE: Intuitive information exploration through interactive visualization

In this paper we present INVISQUE, a novel system designed for interactive information exploration. Instead of a conventional list-style arrangement, in INVISQUE information is represented by a two-dimensional spatial canvas, with each dimension representing user-defined semantics. Search results are presented as index cards, ordered in both dimensions. Intuitive interactions are used to perform tasks such as keyword searching, results browsing, categorizing, and linking to online resources such as Google and Twitter. The interaction-based query style also naturally lends the system to different types of user input such as multi-touch gestures. As a result, INVISQUE gives users a much more intuitive and smooth experience of exploring large information spaces