Carabid Beetles (Coleoptera: Carabidae) as Prey of North American Frogs

In this paper, 62 species of carabid beetles are listed as prey of frogs. Records are from the literature and a stomach contents study. During the period May to October 1973, I collected and examined the stomach contents of 2 bullfrogs, 45 green frogs, 504 northern leopard frogs and 7 wood frogs from southern Quebec in order to learn whether carabid beetles are major or minor prey of frogs

Wing-Dimorphism in Cymindis Cribricollis Dejean and C. Neglecta Haldeman (Coleoptera: Carabidae)

(excerpt) One hundred and forty-nine specimens of Cymindis cribricollis Dejean and fifteen specimens of C. neglecta Haldeman from Quebec were examined for wingdimorphism

Notes on the Food of Cychrini (Coleoptera: Carabidae)

In this paper, the food of nineteen species of Cychrini is given, for three genera: Cychrus, Scaphinotus and Sphaeroderus. The beetles of this tribe seem to be nearly exclusively carnivorous, feeding principally on snails and slugs, exceptionally on insects and vegetable matter. The head is narrow and prolonged; the mandibles are elongate and prominent, with two acute median teeth in outer half, apparently well adapted for entering the opening of a snail shell. The beetles may be useful in keeping down harmful molluscs. The purpose of this study was to compile a list of data on the food of some Cychrini, from the literature and observations in the field. Almost all species live in forest country and appear to be nocturnal. Cychrus caraboides Linne, Cychrus dufouri Chaudoir, Scaphinotus bilobus Say and Sphaeroderus lecontei Dejean have been noted searching for food on rainy days

Market participation and marketing performance: A case study of Bolivian potato farmers

Agribusiness, Marketing,

Practical Bayesian Optimization of Machine Learning Algorithms

Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of developing automatic approaches which can optimize the performance of a given learning algorithm to the task at hand. In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). The tractable posterior distribution induced by the GP leads to efficient use of the information gathered by previous experiments, enabling optimal choices about what parameters to try next. Here we show how the effects of the Gaussian process prior and the associated inference procedure can have a large impact on the success or failure of Bayesian optimization. We show that thoughtful choices can lead to results that exceed expert-level performance in tuning machine learning algorithms. We also describe new algorithms that take into account the variable cost (duration) of learning experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of contemporary algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks