Turfgrass évapotranspiration. R. Tovey, J. S. Spencer and D. C. • Muckel. 1969. Agronomy Journal. 61:863-867. (from the University of Nevada, Reno, Ne v. 89507). The lysimeter technique was utilized to measure the rate of évapotranspi ra t ion from two types of turf grown on two different soil textures. The two types of turfgrass were (a) a cool season turf composed primarily of Kentucky bluegr...

I gather together known results on fundamental solutions to the wave equation in free space, and Greens functions in tori, boxes, and other domains. From this the corresponding fundamental solutions for the Helmholtz equation are derived, and, for the 2D case the semiclassical approximation interpreted back in the time-domain. Utility: scarring via time-dependent propagation in cavities; Math 4...

Establishing and Trafficking New Bentgrass Putting Greens Golf turf managers are pressured from the golfing community to reduce the establishment time of playing surfaces. To this end, accelerated grow-in procedures are implemented that eventually permit play on surfaces that are less than prepared to receive traffic. Research hassuggested that accelerated grow-in procedures and premature traff...

This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise and the estimates is unbounded. The bound applies to many loss functions besides the squared loss, and scales only with the sub-Gaussian or subexponential parameters without further statistical assumptions such as the...

The theoretical framework of Statistical Learning Theory (SLT) for pattern recognition problems is extended to comprehend the situations where an infinite value of the loss function is employed to prevent misclassifications in specific regions with high reliability. Sufficient conditions for ensuring the consistency of the Empirical Risk Minimization (ERM) criterion are then established and an ...

We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random subset of the dual variables. However, unlike existing methods such as stochastic dual coordinate ascent, SDNA is capable of utilizing all local curvature information contained in the examples, which leads to striking impr...

We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a simple known structure over each block. We develop an accelerated randomized proximal coordinate gradient (APCG) method for minimizing such convex composite functions. For strongly convex functions, our method achiev...

We consider the problem of training a conditional random field (CRF) to maximize per-label predictive accuracy on a training set, an approach motivated by the principle of empirical risk minimization. We give a gradient-based procedure for minimizing an arbitrarily accurate approximation of the empirical risk under a Hamming loss function. In experiments with both simulated and real data, our o...

The general problem of reconstructing an unknown function from a finite collection of samples is considered, in case the position of each input vector in the training set is not fixed beforehand, but is part of the learning process. In particular, the consistency of the Empirical Risk Minimization (ERM) principle is analyzed, when the points in the input space are generated by employing a purel...