37 research outputs found
FDI Determination and Corporate Tax Competition in a Volatile World
This paper investigates the role of economic and political volatility in the process of corporate tax-rate determination. The article is based on a theoretical framework that allows for the ability of multinational firms to choose the optimal timing of foreign investment and to shift profits by transfer pricing, and provides an empirical analysis on a large panel data set of countries over the 1983-2003 period. First, a reduced-form dynamic equation of corporate tax rate determination is estimated by the generalised method of moments (GMM), where a countryās top statutory corporate tax rate depends on a number of measures of economic and political volatility. The fundamental testable prediction derived from the theoretical model is that increased volatility should reduce a countryās corporate tax rate. Our results support the hypothesis that economic volatility is associated with lower top statutory corporate tax rates, while our measures of political volatility have no significant impact on corporate taxation policy. In order to identify the channels through which volatility works, we also estimate a structural model allowing for simultaneous determination of the corporate tax rate and the inflow of FDI to a particular country. The estimates of the structural model show that economic volatility affects the corporate tax setting process through their impact on FDI inflow.foreign direct investment, tax competition, volatility
Vehicle Navigation Service Based on Real-Time Traffic Information
GNSS-assisted vehicle navigation services are nowadays very common in most of the developed countries. However, most of those services are either delivered through proprietary technologies, or fall short in flexibility because of the limited capability to couple road information with real-time traffic information. This paper presents the motivations and a brief summary of a vehicle navigation service based on real-time traffic information, delivered through an open protocol that is currently under standardization in the Open Mobile Alliance forum
Inertial sensors forĀ smartphones navigation
The advent of smartphones and tablets, means that we can constantly get informa-
tion on our current geographical location. These devices include not only GPS/GNSS
chipsets but also mass-market inertial platforms that can be used to plan activities,
share locations on social networks, and also to perform positioning in indoor and
outdoor scenarios. This paper shows the performance of smartphones and their inertial
sensors in terms of gaining information about the userās current geographical loca-
tion considering an indoor navigation scenario. Tests were carried out to determine
the accuracy and precision obtainable with internal and external sensors. In terms of
the attitude and drift estimation with an updating interval equal to 1 s, 2D accuracies
of about 15 cm were obtained with the images. Residual benefits were also obtained,
however, for large intervals, e.g. 2 and 5 s, where the accuracies decreased to 50 cm
and 2.2 m, respectively
Cellular network capacity and coverage enhancement with MDT data and Deep Reinforcement Learning
Recent years witnessed a remarkable increase in the availability of data and computing resources in comm-unication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT -driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks
DynNav: Toward Open and Interoperable Dynamic Navigation Services
So far, navigation devices, including navigation apps for smartphones, have been proprietary and closed. A new scenario is emerging with the Open Mobile Alliance Dynamic Navigation Enabler, which lets developers create novel navigation services characterized by openness and interoperability across different information providers
Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements
The advent of novel 5G services and applications with binding latency
requirements and guaranteed Quality of Service (QoS) hastened the need to
incorporate autonomous and proactive decision-making in network management
procedures. The objective of our study is to provide a thorough analysis of
predictive latency within 5G networks by utilizing real-world network data that
is accessible to mobile network operators (MNOs). In particular, (i) we present
an analytical formulation of the user-plane latency as a Hypoexponential
distribution, which is validated by means of a comparative analysis with
empirical measurements, and (ii) we conduct experimental results of
probabilistic regression, anomaly detection, and predictive forecasting
leveraging on emerging domains in Machine Learning (ML), such as Bayesian
Learning (BL) and Machine Learning on Graphs (GML). We test our predictive
framework using data gathered from scenarios of vehicular mobility, dense-urban
traffic, and social gathering events. Our results provide valuable insights
into the efficacy of predictive algorithms in practical applications