The Impact of the Stability and Growth Pact on Real Economic

The recession under way in the European Union and the threat of deflation have spawned increasing frequent calls for modification of the Stability and Growth Pact. The present article confirms the negative correlation of the rate of real output growth with that of increase in current public expenditure, but finds a positive correlation of growth with the rate of increase in public capital spending, private investment, tax to GDP ratio, and an indicator of the net profit rate. The policy prescription is for the urgent modification of the rules of the Pact, exempting public investment from its constraints subject to the assessment of the Ecofin Council. The markets would be receptive to such a change if the EU instituted clear new rules, not just reinterpreting those now in being under the pressure of contingent factors. On this basis, we find that Italy's economic crisis is due in part to the misconceived fiscal and monetary policy rules of the European Union.Stability Pact, Fiscal Rules, European Union, Ricardian Equivalence

The impact on the U.S. Dollar of the conflict between the American locomotive’s model and the emerging economies’ autopoietic growth.

The purpose of this paper is to put the future of the US dollar into a logical framework which comprises the global development mechanism. Two models of growth collide: the US «locomotive», based on the international use of the dollar, and which requires exogenous pushes coming permanently from the foreign deficit and periodically from the public deficit, and the «endogenous», or «autopoietic». The engine of autopoietic growth is the process of globalization, alimented by foreign investments and the emerging economies’ domestic demand, which in turn require the establishment of an international monetary standard. In absence of a real international cooperation, the conflict of the two models might bring a global currency crisis and a fall in the global growth rate, with a possible negative impact in foreign relations and policies at the global level.International monetary system, Dollar, Euro, Exchange rate, Economic growth, International Finance, International Political Economy.

Deep Learning at Scale with Nearest Neighbours Communications

As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist’s Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework

Planning short pointing sequences

An experiment tested the hypothesis that fast, short sequences of movements are planned as a whole, before movement inception. The experimental task consisted of pointing to either one (one-step condition), or two (two-step condition) visual targets aligned along the mid-sagittal axis in a horizontal plane. There were nine possible arrangements of the targets resulting from all combinations of three distances (5, 10, 15cm), and two trial orders (blocked or random). Performances were characterised by reaction time (RT), movement kinematics, and spatial accuracy. Compared with one-step trials, the first movements of two-step trials had longer RTs (length effect), particularly in random sessions, and when the sequences included short-distance targets. There were also differences in duration (one-target advantage), velocity profile and spatial accuracy that did not depend on the characteristics of the second movement. The results are inconsistent with the assumption that two-step sequences are planned as a whole. Instead, they are in keeping with the alternative hypothesis that part of the preparation of the second step takes place during the execution of the first ste

Amplitude and direction errors in kinesthetic pointing

We investigated the accuracy with which, in the absence of vision, one can reach again a 2D target location that had been previously identified by a guided movement. A robotic arm guided the participant's hand to a target (locating motion) and away from it (homing motion). Then, the participant pointed freely toward the remembered target position. Two experiments manipulated separately the kinematics of the locating and homing motions. Some robot motions followed a straight path with the bell-shaped velocity profile that is typical of natural movements. Other motions followed curved paths, or had strong acceleration and deceleration peaks. Current motor theories of perception suggest that pointing should be more accurate when the homing and locating motion mimics natural movements. This expectation was not borne out by the results, because amplitude and direction errors were almost independent of the kinematics of the locating and homing phases. In both experiments, participants tended to overshoot the target positions along the lateral directions. In addition, pointing movements towards oblique targets were attracted by the closest diagonal (oblique effect). This error pattern was robust not only with respect to the manner in which participants located the target position (perceptual equivalence), but also with respect to the manner in which they executed the pointing movements (motor equivalence). Because of the similarity of the results with those of previous studies on visual pointing, it is argued that the observed error pattern is basically determined by the idiosyncratic properties of the mechanisms whereby space is represented internall

Factors affecting the size of the detour effect in the kinaesthetic perception of Euclidean distance

Three experiments investigated the mechanisms by which we estimate Euclidean distances on the basis of kinaesthetic cues. In all experiments, blindfolded participants followed straight and curvilinear paths with a stylus. Then, with a straight response movement, they estimated the distance between the end-points of the previously explored path. Experiment 1 was designed to validate the hypothesis—made on the basis of results from a previous study—that errors in the kinaesthetic estimations of distances (detour effect) originate from the difficulty to decompose the displacement vector into relevant and irrelevant components, which would become more severe at points of inflection. Using elliptic paths (no inflections), we demonstrated that errors are indeed reduced considerably. The role of the orientation of the work plane was investigated in Experiment 2 in which the same paths used in our previous study were oriented in the frontal rather than the horizontal plane. The results indicate that the detour effect is independent of the orientation. Moreover, despite the asymmetry that gravity introduces between upward and downward movements, errors in the two directions are almost identical. Experiment 3 addressed two issues. First, we demonstrated that introducing a delay between the exploration of the path and the response did not alter significantly the pattern of errors. By contrast, we demonstrated that errors are severely reduced when the number of paths to be explored is reduced by half. The results of the three experiments are discussed within the context of current theories of sensori-motor codin

The kinaesthetic perception of Euclidean distance: a study of the detour effect

An experiment investigated the mechanisms by which humans estimate Euclidean distances on the basis of kinaesthetic cues. Blindfolded participants followed straight and curvilinear paths with a hand-held stylus (encoding phase). Then, with a straight movement, they estimated the Euclidean distance between the start- and end-points of the path (response phase). The experiment contrasted an On-axis condition, in which encoding and response movements were spatially aligned, and an Off-axis condition, in which they were displaced laterally. Performances were slightly more accurate in the On-axis condition than in the Off-axis condition. In both conditions, however, errors were consistently smaller when the path covered a larger surface. The results showed that small paths yielded an overestimation of the Euclidean distance, the relative errors increasing with the length of curvilinear paths. The findings are compared with results of other studies in which distances were estimated on the basis of haptic cue

An efficient geometric method for incompressible hydrodynamics on the sphere

We present an efficient and highly scalable geometric method for two-dimensional ideal fluid dynamics on the sphere. The starting point is Zeitlin's finite-dimensional model of hydrodynamics. The efficiency stems from exploiting a tridiagonal splitting of the discrete spherical Laplacian combined with highly optimized, scalable numerical algorithms. For time-stepping, we adopt a recently developed isospectral integrator able to preserve the geometric structure of Euler's equations, in particular conservation of the Casimir functions. To overcome previous computational bottlenecks, we formulate the matrix Lie algebra basis through a sequence of tridiagonal eigenvalue problems, efficiently solved by well-established linear algebra libraries. The same tridiagonal splitting allows for computation of the stream matrix, involving the inverse Laplacian, for which we design an efficient parallel implementation on distributed memory systems. The resulting overall computational complexity is $\mathcal{O}(N^3)$ per time-step for $N^2$ spatial degrees of freedom. The dominating computational cost is matrix-matrix multiplication, carried out via the parallel library ScaLAPACK. Scaling tests show approximately linear scaling up to around $2500$ cores for the matrix size $N=4096$ with a computational time per time-step of about $0.55$ seconds. These results allow for long-time simulations and the gathering of statistical quantities while simultaneously conserving the Casimir functions. We illustrate the developed algorithm for Euler's equations at the resolution $N=2048$