Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent

The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing perceptron learning algorithms. In this paper, we propose a family of random coordinate descent algorithms to directly minimize the 0/1 loss for perceptrons, and prove their convergence. Our algorithms are computationally efficient, and usually achieve the lowest 0/1 loss compared with other algorithms. Such advantages make them favorable for nonseparable real-world problems. Experiments show that our algorithms are especially useful for ensemble learning, and could achieve the lowest test error for many complex data sets when coupled with AdaBoost

OPE of the stress tensors and surface operators

We demonstrate that the divergent terms in the OPE of a stress tensor and a surface operator of general shape cannot be constructed only from local geometric data depending only on the shape of the surface. We verify this holographically at d=3 for Wilson line operators or equivalently the twist operator corresponding to computing the entanglement entropy using the Ryu-Takayanagi formula. We discuss possible implications of this result.Comment: 20 pages, no figur

Assisted optimal state discrimination without entanglement

A fundamental problem in quantum information is to explore the roles of different quantum correlations in a quantum information procedure. Recent work [Phys. Rev. Lett., 107 (2011) 080401] shows that the protocol for assisted optimal state discrimination (AOSD) may be implemented successfully without entanglement, but with another correlation, quantum dissonance. However, both the original work and the extension to discrimination of $d$ states [Phys. Rev. A, 85 (2012) 022328] have only proved that entanglement can be absent in the case with equal a \emph{priori} probabilities. By improving the protocol in [Sci. Rep., 3 (2013) 2134], we investigate this topic in a simple case to discriminate three nonorthogonal states of a qutrit, with positive real overlaps. In our procedure, the entanglement between the qutrit and an auxiliary qubit is found to be completely unnecessary. This result shows that the quantum dissonance may play as a key role in optimal state discrimination assisted by a qubit for more general cases.Comment: 6 pages, 3 figures. Accepted by EPL. We extended the protocol for assisted optimal state discrimination to the case with positive real overlaps, and presented a proof for the absence of entanglemen

Calibration and Validation of the Ibsnat/Ceres Rice Model

Two rice varieties were subjected to two nitrogen rates and three temperature regimes in the greenhouse and growth chambers to study the effects of temperature, variety and N fertilization on N uptake, development and growth of rice. Nitrogen fertilization had a significant effect on grain and straw yields for both varieties. High nitrogen application resulted in high grain yield and N stress reduced biomass production but had no effect on the timing of phonological events of variety Starbonnet, but delayed panicle initiation in variety K-C-A. Temperature affected grain yield and nitrogen uptake during the grain filling stage. High day and night temperature hastened maturation and resulted in lower filled grain percentage, lower 1,000-grain weight and lower overall grain yield. Nitrogen concentration and N uptake were higher in the higher temperature. However, the persistence of green color and a low ratio of grain N to straw N indicate that nitrogen translocation from straw to grain was diminished by the high temperature. The IBSNAT/CERES Rice Model was calibrated and validated with data collected from field experiments under a wide range of agroenvironments. The model was able to adequately predict phenological development for a wide range of agroenvironments. Model prediction of final biomass was also acceptable. The model is sensitive to seasonal variation and altitudinal difference and is able to mimic the high sensitivity of rice to temperature and solar radiation

Ordinal Regression by Extended Binary Classification

We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranking rule from the binary classifier. A weighted 0/1 loss of the binary classifier would then bound the mislabeling cost of the ranking rule. Our framework allows not only to design good ordinal regression algorithms based on well-tuned binary classification approaches, but also to derive new generalization bounds for ordinal regression from known bounds for binary classification. In addition, our framework unifies many existing ordinal regression algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms, which demonstrates the usefulness of our framework