Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text classification machine learning model. One of the methods includes training a model having a plurality of parameters and configured to generate a classification of a text sample comprising a plurality of words by processing a model input that includes a combined feature representation of the plurality of words in the text sample, wherein the training comprises receiving a text sample and a target classification for the text sample; generating a plurality of perturbed combined feature representations; determining, based on the plurality of perturbed combined feature representations, a region in the embedding space; and determining an update to the parameters based on an adversarial objective that encourages the model to assign the target classification for the text sample for all of the combined feature representations in the region in the embedding space.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text classification machine learning model. One of the methods includes training a model having a plurality of parameters and configured to generate a classification of a text sample comprising a plurality of words by processing a model input that includes a combined feature representation of the plurality of words in the text sample, wherein the training comprises receiving a text sample and a target classification for the text sample; generating a plurality of perturbed combined feature representations; determining, based on the plurality of perturbed combined feature representations, a region in the embedding space; and determining an update to the parameters based on an adversarial objective that encourages the model to assign the target classification for the text sample for all of the combined feature representations in the region in the embedding space.

Abstract: Processes for reducing hexavalent chromium, Cr(VI) in a chromite ore processing residue matrix and processes for analyzing and determining effective treatment are disclosed.

Abstract: Processes for reducing hexavalent chromium, Cr(VI) in a chromite ore processing residue matrix and processes for analyzing and determining effective treatment are disclosed.

Abstract: A process for reducing hexavalent chromium, Cr(VI), contained within a chromite ore processing residue matrix comprising the sequential steps of providing a chromite ore processing residue matrix containing Cr(VI), solubilizing the matrix to release Cr(VI), reducing the Cr(VI) to Cr(III) using Fe(II), and, fixing the residual Fe(II) using a effective amount of a Fe(II) precipitating agent to make a Fe(II) precipitate.

Abstract: Methods of using micronized particulate, reactive magnesium oxide or magnesium hydroxide to treat hazardous waste having high zinc content and hazardous levels of cadmium and lead. The reactive magnesium oxide or magnesium hydroxide has a median particle size in the range of 2-3 ?m. The hazardous waste material is generated by a foundry or steel mill.

Abstract: A method of treating a waste material or soil by contacting the waste material or soil with an effective amount of corn ash to lower metal leaching in a Toxicity Characteristics Leaching Procedure test to below the hazardous waste characteristic criteria producing a treated waste material or soil, wherein the corn ash contains an effective amount of one or more orthophsophates. Preferably, the corn ash is substantially free of polyphosphates. The waste material or soil is a hazardous waste containing one or more metals being Cd, Pb and Zn. The waste material is generated by a foundry or steel mill.

Abstract: A method for estimation of the distance from a domain by means of a fragment-based model, the method comprising the steps of identifying the fragments [r1 to rn] in a structure, comparing the or each fragment with one or more fragments in the model, if the or each fragment substantially matches a fragment in a model determining a first error measure between a contribution of the or each fragment and a contribution of the matching fragment in the model, if the or each fragment does not substantially match a fragment in the model determining which fragment in the model is the most similar to the or each fragment and determining a second error measure based on the similarity between the fragment and the most similar fragment; and combining the first error measure and the second error measure to generate a degree of separation between the activity of the structure and of the combined contribution of the fragments in the model.