37,671 research outputs found

    Efficiency in Water and Sanitation Sector. A Survey on Empirical Literature

    Get PDF
    In this paper, it was made an exhaustive survey of the literature related with cost and production frontiers in the water and sanitation sector. The survey shed light in order to determine the variables to choose in the model to be estimated in a further empirical estimation developed for the Latin American Region by the authorsfrontiers; water and sanitation sector; empirical estimation

    The marginally stable Bethe lattice spin glass revisited

    Full text link
    Bethe lattice spins glasses are supposed to be marginally stable, i.e. their equilibrium probability distribution changes discontinuously when we add an external perturbation. So far the problem of a spin glass on a Bethe lattice has been studied only using an approximation where marginally stability is not present, which is wrong in the spin glass phase. Because of some technical difficulties, attempts at deriving a marginally stable solution have been confined to some perturbative regimes, high connectivity lattices or temperature close to the critical temperature. Using the cavity method, we propose a general non-perturbative approach to the Bethe lattice spin glass problem using approximations that should be hopeful consistent with marginal stability.Comment: 23 pages Revised version, hopefully clearer that the first one: six pages longe

    Petuum: A New Platform for Distributed Machine Learning on Big Data

    Full text link
    What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, allowing ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl

    Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

    Full text link
    We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.Comment: 31 pages (one column), 9 figure

    Animal Efficiency in an Intensive Beef Production System

    Get PDF
    A stochastic input distance function is estimated to analyse the efficiency with which physical characteristics of individual lot-fed beef cattle in Australia are combined with conventional inputs to produce a final product possessing defined quality attributes. High mean technical efficiency estimates are reported for all animals and by breed. All partial output elasticities with respect to inputs are of expected sign. Of four outputs included in the analysis, carcass weight and moisture retention in meat after cooking have highly significant coefficients of expected sign, but two meat quality variables have coefficients of unexpected sign indicating that they decline as inputs increase. Some evidence is detected of scope economies between moisture retention in meat and the inverse of meat compression.efficiency, intensive agriculture, scope economies, Livestock Production/Industries, Q12, C51,

    Sequential Design for Ranking Response Surfaces

    Full text link
    We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given L≥2L \ge 2 response surfaces over a continuous input space X\cal X, the aim is to efficiently find the index of the minimal response across the entire X\cal X. The response surfaces are not known and have to be noisily sampled one-at-a-time. This setting is motivated by stochastic control applications and requires joint experimental design both in space and response-index dimensions. To generate sequential design heuristics we investigate stepwise uncertainty reduction approaches, as well as sampling based on posterior classification complexity. We also make connections between our continuous-input formulation and the discrete framework of pure regret in multi-armed bandits. To model the response surfaces we utilize kriging surrogates. Several numerical examples using both synthetic data and an epidemics control problem are provided to illustrate our approach and the efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
    • …
    corecore