Search results for “integrals”

Reference table of the basic indefinite integrals of the elementary functions: power, exponential, logarithmic and trigonometric.

Reference table of indefinite integrals of the exponential function and its products with powers of x.

Reference table of indefinite integrals of the hyperbolic sine, cosine, tangent and cotangent.

Reference table of indefinite integrals of expressions containing the natural logarithm.

Reference table of indefinite integrals of rational functions, including quadratic denominators and partial-fraction forms.

Reference table of indefinite integrals of expressions containing square roots and other radicals.

Reference table of indefinite integrals of products of exponentials with sine and cosine.

Reference table of indefinite integrals of products of powers of x with sine and cosine.

Reference table of indefinite integrals of sine, cosine, tangent and their powers and products.

A pilot study mapping ion-induced charge collection against bandgap and carrier mobility, and what it revealed about the cost of getting the time integration wrong

Modern machine translation systems are trained on large volumes of parallel data obtained using heuristic methods of bypassing the Internet. The poor quality of the data leads to systematic translation errors, which can be quite noticeable to humans. To fix such errors, human-based models for reranking hypotheses is introduced in this study. In this paper the use of human markup is shown not only to increase the overall quality of the translation but also to significantly reduce the number of systematic translation errors. In addition, the relative simplicity of human markup and its integration in the model training process opens up new opportunities in the field of domain adaptation of translation models for new domains like online retail.

Modern machine translation systems are trained on large volumes of parallel data obtained using heuristic methods of the Internet bypassing. The poor quality of the data leads to systematic translation errors, which can be quite noticeable from the human point of view. To fix such errors a human based models hypotheses re-ranking is introduced in this work. In this paper the use of human markup is shown not only to increase the overall quality of translation, but also to significantly reduce the number of systematic translation errors. In addition, the relative simplicity of human markup and its integration in the model training process opens up new opportunities in the field of domain adaptation of translation models for new domains like online retail.