384,080 research outputs found

    The transport in our time-budget

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    Do we save time with our faster transport modes? The answer is, no. The authors answered this question after comparing the average daily per capita transport time-use based on the 1986/87, the 1999/00, and the 2009/10 Hungarian time-budget survey. The average time-use remained between 60 and 65 minutes, same as it was in 1977. During the period studied, the share of the motor/car time-use approximately doubled in the total transport time-use, while the other modes (walking, cycling, and public transports) decreased proportionally. In the same period, there was a wide distribution in the per capita daily transport time-use data influenced by geographical destination choice (in space and time)—and by demographic (age, gender), spatial (county, settlement status), and social (activity, qualification) variables. The paper analysed the effect of the latter explanatory variables on the heterogeneity of the transport time-use. The gender and activity variables can explain motor/car time-use differences; geography and settlement status the bicycle-, and the settlement status also the public transport time-use differences. However, all the explanatory variables analysed could only explain 10% of the divergences

    The transport in our time-budget

    Get PDF
    Do we save time with our faster transport modes? The answer is, no. The authors answered this question after comparing the average daily per capita transport time-use based on the 1986/87, the 1999/00, and the 2009/10 Hungarian time-budget survey. The average time-use remained between 60 and 65 minutes, same as it was in 1977. During the period studied, the share of the motor/car time-use approximately doubled in the total transport time-use, while the other modes (walking, cycling, and public transports) decreased proportionally. In the same period, there was a wide distribution in the per capita daily transport time-use data influenced by geographical destination choice (in space and time)—and by demographic (age, gender), spatial (county, settlement status), and social (activity, qualification) variables. The paper analysed the effect of the latter explanatory variables on the heterogeneity of the transport time-use. The gender and activity variables can explain motor/car time-use differences; geography and settlement status the bicycle-, and the settlement status also the public transport time-use differences. However, all the explanatory variables analysed could only explain 10% of the divergences

    The Impact of Fiscal Policy on Profits

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    This paper investigates the impact of fiscal policy on profits using panel data for 19 high-income OECD countries during the period 1975-1999. We estimate a profit equation in which profits depend on a set of fiscal variables. Our empirical method is based on a consistent treatment of the government budget constraint, and we try to disentangle the effects of different spending and taxation items. As far as public spending is concerned, our results strongly suggest that capital expenditures are associated with higher profits, while expenditures on wages and salaries deteriorate profits. At the same time our results indicate that transport and communication expenditures increase profits, while the opposite holds for defense expenditures. On the revenue side, both direct and indirect taxation tend to decrease profits. However, a more detailed sub-division of direct taxation indicates that social security contributions have a neutral effect on profits.fiscal policy, profits, quality of public expenditure

    Unsteady feeding and optimal strokes of model ciliates

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    The flow field created by swimming microorganisms not only enables their locomotion but also leads to advective transport of nutrients. In this paper we address analytically and computationally the link between unsteady feeding and unsteady swimming on a model microorganism, the spherical squirmer, actuating the fluid in a time-periodic manner. We start by performing asymptotic calculations at low P\'eclet number (Pe) on the advection-diffusion problem for the nutrients. We show that the mean rate of feeding as well as its fluctuations in time depend only on the swimming modes of the squirmer up to order Pe^(3/2), even when no swimming occurs on average, while the influence of non-swimming modes comes in only at order Pe^2. We also show that generically we expect a phase delay between feeding and swimming of 1/8th of a period. Numerical computations for illustrative strokes at finite Pe confirm quantitatively our analytical results linking swimming and feeding. We finally derive, and use, an adjoint-based optimization algorithm to determine the optimal unsteady strokes maximizing feeding rate for a fixed energy budget. The overall optimal feeder is always the optimal steady swimmer. Within the set of time-periodic strokes, the optimal feeding strokes are found to be equivalent to those optimizing periodic swimming for all values of the P\'eclet number, and correspond to a regularization of the overall steady optimal.Comment: 26 pages, 11 figures, to appear in Journal of Fluid Mechanic

    Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes

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    Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair

    Radioactivity and thermalization in the ejecta of compact object mergers and their impact on kilonova light curves

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    One of the most promising electromagnetic signatures of compact object mergers are kilonovae: approximately isotropic radioactively-powered transients that peak days to weeks post-merger. Key uncertainties in modeling kilonovae include the emission profiles of the radioactive decay products---non-thermal beta- and alpha-particles, fission fragments, and gamma-rays---and the efficiency with which they deposit their energy in the ejecta. The total radioactive energy and the efficiency of its thermalization sets the luminosity budget and is therefore necessary for predicting kilonova light curves. We outline the uncertainties in r-process decay, describe the physical processes by which the energy of the decay products is absorbed in the ejecta, and present time-dependent thermalization efficiencies for each particle type. We determine the net heating efficiency and explore its dependence on r-process yields---in particular, the production of translead nuclei that undergo alpha-decay---and on the ejecta's mass, velocity, composition, and magnetic field configuration. We incorporate our results into new time-dependent, multi-wavelength radiation transport simulations, and calculate updated predictions of kilonova light curves. Thermalization has a substantial effect on kilonova photometry, reducing the luminosity by a factor of roughly 2 at peak, and by an order of magnitude or more at later times (15 days or more after explosion). We present simple analytic fits to time-dependent net thermalization efficiencies, which can easily be used to improve light curve models. We briefly revisit the putative kilonova that accompanied gamma ray burst 130603B, and offer new estimates of the mass ejected in that event. We find that later-time kilonova light curves can be significantly impacted by alpha-decay from translead isotopes; data at these times may therefore be diagnostic of ejecta abundances.Comment: Submitted to ApJ; comments welcom

    The impact of fiscal policy on profits

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    This paper investigates the impact of fiscal policy on profits using panel data for 19 high-income OECD countries during the period 1975-1999. We estimate a profit equation in which profits depend on a set of fiscal variables. Our empirical method is based on a consistent treatment of the government budget constraint, and we try to disentangle the effects of different spending and taxation items. As far as public spending is concerned, our results strongly suggest that capital expenditures are associated with higher profits, while expenditures on wages and salaries deteriorate profits. At the same time our results indicate that transport and communication expenditures increase profits, while the opposite holds for defense expenditures. On the revenue side, both direct and indirect taxation tend to decrease profits. However, a more detailed sub-division of direct taxation indicates that social security contributions have a neutral effect on profits

    Speeding Up Reachability Queries in Public Transport Networks Using Graph Partitioning

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    Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution

    An Efficient Index for Reachability Queries in Public Transport Networks

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    Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution
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