Asynchronous Gossip for Averaging and Spectral Ranking

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Ranking general circulation models for India using TOPSIS

Eleven general circulation models/global climate models (GCMs) - BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1 - are evaluated for Indian climate conditions using the performance indicator, skill score (SS). Two climate variables, temperature T (at three levels, i.e. 500, 700, 850 mb) and precipitation rate (Pr) are considered resulting in four SS-based evaluation criteria (T500, T700, T850, Pr). The multicriterion decision-making method, technique for order preference by similarity to an ideal solution, is applied to rank 11 GCMs. Efforts are made to rank GCMs for the Upper Malaprabha catchment and two river basins, namely, Krishna and Mahanadi (covered by 17 and 15 grids of size 2.5 degrees x 2.5 degrees, respectively). Similar efforts are also made for India (covered by 73 grid points of size 2.5 degrees x 2.5 degrees) for which an ensemble of GFDL2.0, INGV-ECHAM4, UKMO-HADCM3, MIROC3, BCCR-BCCM2.0 and GFDL2.1 is found to be suitable. It is concluded that the proposed methodology can be applied to similar situations with ease.

STREAM Journal, Vol. 2, No. 4, pp 1-18. October-December 2003

CONTENTS: An experience with group formation in Jabarrah, West Bengal, by Amar Prasad, Virendra Singh, Binay Sahay and Gautum Dutta. Aquaculture self-help groups in rural West Bengal, by Satyendra Tripathi, Graham Haylor and William Savage in consultation with Jagdish Gangwar, Virendra Singh, Gautam Dutta and Prabhat Pathak. Lessons learned for Fulwar Toli from an exposure visit to Jabarrah Bhim Nayak and Rubu Mukherjee. Livelihood intervention by Fish Farmers Development Agency: a success story from Mayurbhanj, Orissa, by G.B. Parida. Understanding of assets-based livelihoods through participatory rural appraisal to eliminate hunger, by Binay Sahay. Poverty ranking tools in PRA: experiences of EIRFP in addressing vulnerability, by Binay Sahay.

Ranking binder creep performance using the arrb elastometer

This paper discusses a laboratory study used to characterize bituminous binders based on their dynamic creep resistance. Laboratory testing using four different loading regimes on asphalt mixes with six different bituminous binders was undertaken. Creep cycles to 2% accumulated strain were used to define the creep resistance of the asphalt mixes with the various binders. Underlying viscosities of the bitumens were derived using the Australian Road Research Board (ARRB) Elastometer. Marshall stability was measured on the specimens that were prepared using gyratory compaction. Regression plots were prepared that link creep resistance, underlying viscosity, and Marshall stability. It was found that the ARRB Elastometer is able to measure underlying viscosity, which is a reasonable predictor of dynamic creep resistance. Marshall stability was also shown to be a good indicator of dynamic creep resistance. Therefore, simpler tests such as Marshall stability and Elastometer can be used to rank bituminous materials for asphalt mix design purposes in the laboratory. © 2010 ASCE.

The ranking of alternative tariff and quota policies in the presence of domestic monopoly

Bhagwati demonstrated the nonequivalence between tariffs and quotas in the presence of monopoly. This paper also assumes domestic production to be monopolized and shows that giving import licenses or tariff revenues to the domestic producer may raise or lower the welfare cost of protection and the price paid by consumers from the price under other tariff and quota arrangements which maintain the same market share for the domestic producer. However, if the monopolist realizes that commercial policy is an instrument used to maximize the policymaker's welfare function, instead of being a goal in itself, the equivalence of tariffs and quotas re-emerges. © 1977.

The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

Ranking de 2008 en productividad en investigaci??n de las universidades p??blicas espa??olas

Resumen tomado de la publicaci??n

Divulgación on-line de los códigos de buen gobierno en las IES y su determinación para el posicionamiento en el ranking QS

Dado el interés que se presenta con los temas de gobierno corporativo, este trabajo busca describir si la divulgación on-line de los contenidos de los códigos de buen gobierno, es determinante en el posicionamiento que tienen las Instituciones de Educación Superior (IES) en el ranking QS. Partiendo de una muestra de 20 IES, se recolectaron un conjunto de datos dicotómicos para 30 variables independientes y se relacionaron con la variable dependiente denominada posicionamiento en el ranking. A partir de lo anterior, se elaboró un trabajo descriptivo y correlacional con el fin de probar las hipótesis de investigación. Este estudio reveló que la divulgación on-line de los contenidos de los códigos de buen gobierno en las IES, no es determinante para el posicionamiento en el ranking QS.

Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data

This article explores how data envelopment analysis (DEA), along with a smoothed bootstrap method, can be used in applied analysis to obtain more reliable efficiency rankings for farms. The main focus is the smoothed homogeneous bootstrap procedure introduced by Simar and Wilson (1998) to implement statistical inference for the original efficiency point estimates. Two main model specifications, constant and variable returns to scale, are investigated along with various choices regarding data aggregation. The coefficient of separation (CoS), a statistic that indicates the degree of statistical differentiation within the sample, is used to demonstrate the findings. The CoS suggests a substantive dependency of the results on the methodology and assumptions employed. Accordingly, some observations are made on how to conduct DEA in order to get more reliable efficiency rankings, depending on the purpose for which they are to be used. In addition, attention is drawn to the ability of the SLICE MODEL, implemented in GAMS, to enable researchers to overcome the computational burdens of conducting DEA (with bootstrapping).

Bayesian ranking and selection of fishing boat efficiencies

The steadily accumulating literature on technical efficiency in fisheries attests to the importance of efficiency as an indicator of fleet condition and as an object of management concern. In this paper, we extend previous work by presenting a Bayesian hierarchical approach that yields both efficiency estimates and, as a byproduct of the estimation algorithm, probabilistic rankings of the relative technical efficiencies of fishing boats. The estimation algorithm is based on recent advances in Markov Chain Monte Carlo (MCMC) methods—Gibbs sampling, in particular—which have not been widely used in fisheries economics. We apply the method to a sample of 10,865 boat trips in the US Pacific hake (or whiting) fishery during 1987–2003. We uncover systematic differences between efficiency rankings based on sample mean efficiency estimates and those that exploit the full posterior distributions of boat efficiencies to estimate the probability that a given boat has the highest true mean efficiency.

Time series multistep-ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices.

Identification and ranking of extraordinary rainfall events over northwest Italy: the role of Atlantic moisture

An objective identification and ranking of extraordinary rainfall events for Northwest Italy is established using time series of annual precipitation maxima for 1938–2002 at over 200 stations. Rainfall annual maxima are considered for five reference durations (1, 3, 6, 12, and 24 h). In a first step, a day is classified as an extraordinary rainfall day when a regional threshold calculated on the basis of a two-components extreme value distribution is exceeded for at least one of the stations. Second, a clustering procedure taking into account the different rainfall durations is applied to the identified 163 events. Third, a division into six clusters is chosen using Ward's distance criteria. It is found that two of these clusters include the seven strongest events as quantified from a newly developed measure of intensity which combines rainfall intensities and spatial extension. Two other clusters include the weakest 72% historical events. The obtained clusters are analyzed in terms of typical synoptic characteristics. The two top clusters are characterized by strong and persistent upper air troughs inducing not only moisture advection from the North Atlantic into the Western Mediterranean but also strong northward flow towards the southern Alpine ranges. Humidity transports from the North Atlantic are less important for the weaker clusters. We conclude that moisture advection from the North Atlantic plays a relevant role in the magnitude of the extraordinary events over Northwest Italy.