Improving the Quality of the Input in the Term Structure Consistent Models

In finance, getting an accurate estimation of the term structure of interest rates is essential because this information is often used as input by other pricing financial models. In this paper, we point out the importance of selecting a suitable estimation of the term structure of interest rates. To show this fact, we use the Spanish Bond Market to estimate the initial interest rate and forward curves for one day, by using both McCulloch (1975) cubic polynomial splines, and Legendre's polynomials (Morini, 1998). We use these curves as input for pricing pure discount bonds with the Ho and Lee (1986) and Hull and White (1990) models. Then, we find the important result that using an inadequate interest rate curve affects dramatically the behaviour of the dynamic term structure models and, consequently, the estimation of the asset pricing modelsTerm structure of interest rates, dynamic consistent models

How much can Syntax help Sentence Compression ?

International audienceSentence compression involves selecting key information present in the input and rewriting this information into a short, coherent text. Using the Gigaword corpus, we provide a detailed investigation of how syntax can help guide both extractive and abstractive sentence compression. We explore different ways of selecting subtrees from the dependency structure of the input sentence; compare the results of various models and show that preselecting information based on syntax yields promising results

Federal Regulation of Non-Nuclear Hazardous Wastes: A Research Bibliography

The identification of nonlinear systems by the minimization of a predictionerror criterion suffers from the problem of local minima. To get a reliableestimate we need good initial values for the parameters. In this paper wediscuss the class of nonlinear Wiener models, consisting of a linear dynamicsystem followed by a static nonlinearity. By selecting a parameterizationwhere the parameters enter linearly in the error, we can obtain an initialestimate of the model via linear regression. An example shows that thisapproach may be preferential to trying to estimate the linear system directlyform input-output data, if the input is not Gaussian. We discuss some of theusers choices and how the linear regression initial estimate can be convertedto a desired model structure to use in the prediction error criterionminimization. The method is also applied to experimental data

Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders

We show that a Modular Neural Network (MNN) can combine various speech enhancement modules, each of which is a Deep Neural Network (DNN) specialized on a particular enhancement job. Differently from an ordinary ensemble technique that averages variations in models, the propose MNN selects the best module for the unseen test signal to produce a greedy ensemble. We see this as Collaborative Deep Learning (CDL), because it can reuse various already-trained DNN models without any further refining. In the proposed MNN selecting the best module during run time is challenging. To this end, we employ a speech AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as similar as possible if its input is clean speech. Therefore, the AE can gauge the quality of the module-specific denoised result by seeing its AE reconstruction error, e.g. low error means that the module output is similar to clean speech. We propose an MNN structure with various modules that are specialized on dealing with a specific noise type, gender, and input Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always works better than an arbitrarily chosen DNN module and sometimes as good as an oracle result

Machine Learning to Select Input Language on a Software Keyboard

Generally, the present disclosure is directed to selecting an input language on a software keyboard. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an input language for a keyboard based on device usage data

Earthquake Input Motions for Physical Model Tests

The results from several dynamic centrifuge experiments are presented in this paper; the experiments were performed as part of a study to assess the influence of local site conditions on earthquake ground motions. Medium dense dry sand and saturated soft clay models were subjected to the accelerogram recorded at Santa Cruz during the 1989 Loma Prieta Earthquake. Scaled versions of the input motion were used to shake the soil models; in addition, different time steps were used in order to study the effects of frequency content of the input motion. The results confirm that the characteristics of the input motion and the soil model combine to have important effects on soil response. This fact must be recognized when selecting input motions for physical model tests