Is Argentina Ready for the New International Tax Era?

We are facing a new era in the international tax system. Even though we have achieved consensus in many areas, adopting multilateral solutions or standardizing by establishing minimum standards, we are also facing unilateral measures due to a lack of consensus in other very sensitive areas. Argentina’s approach toward the international tax system has always been very dependent on the local politics. In very recent years, the objective of becoming a member of the OECD has made Argentina its best student. But it has not always been this way. To understand if Argentina is ready to face the new era that the international tax system is going through, we will review four relevant—and capriciously chosen—aspects of Argentine international tax rules, taking into account the evolution, current situation, and effects. We will analyze the tax treaty network, transparency policy, CFC rules, and taxation of digital economy

Towards a neural hierarchy of time scales for motor control

Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control

Differences in life expectancy between four Western countries and their Caribbean dependencies, 1980–2014

BackgroundIn the Caribbean, life expectancy in politically independent territories has increasingly diverged from that of territories that remained affiliated to their former colonizers. Because these affiliated territories differ in degree of political independence, they are not all governed in the same way. We assessed whether differences in life expectancy trends between Caribbean dependencies and their Western administrators were related to their degree of political independence, and which causes of death contributed to divergence or convergence in life expectancy.MethodsAnalysis of age-standardized death rates and decomposition of life expectancy differences between France, the Netherlands, UK, USA and their Caribbean dependencies by age and cause-of-death during the period 1980–2014.ResultsLife expectancy differences between Western countries and their dependencies have generally increased for men and narrowed for women, but trends have been much more favorable in the French- than in the Dutch-administered territories. The strongest contributions to widening gaps in life expectancy between Western countries and their dependencies were from mortality from cardiovascular diseases (ischemic heart disease) and external causes (homicide and traffic accidents).ConclusionDependencies with a stronger political affiliation to a Western country experienced more favorable life expectancy developments than dependencies that had more autonomy during the 1980–2014 period. The underlying mortality differences with Western countries are largely comparable among Caribbean territories but differ in magnitude, most notably for cardiovascular disease and external causes. This suggests that increases in a territory’s political autonomy impairs the diffusion of new knowledge and techniques, and/or reduces government’s effectiveness in implementing policies

Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation

Information processing using a single dynamical node as complex system

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing