Norm Extrapolation Research Articles

Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection.

Data on the emotionality of words is important for the selection of experimental stimuli and sentiment analysis on large bodies of text. While norms for valence and arousal have been thoroughly collected in English, most languages do not have access to such large datasets. Moreover, theoretical developments lead to new dimensions being proposed, the norms for which are only partially available. In this paper, we propose a transformer-based neural network architecture for semantic and emotional norms extrapolation that predicts a whole ensemble of norms at once while achieving state-of-the-art correlations with human judgements on each. We improve on the previous approaches with regards to the correlations with human judgments by Δr = 0.1 on average. We precisely discuss the limitations of norm extrapolation as a whole, with a special focus on the introduced model. Further, we propose a unique practical application of our model by proposing a method of stimuli selection which performs unsupervised control by picking words that match in their semantic content. As the proposed model can easily be applied to different languages, we provide norm extrapolations for English, Polish, Dutch, German, French, and Spanish. To aid researchers, we also provide access to the extrapolation networks through an accessible web application.

The design of visco‐acoustic weighted L1‐error frequency–space wavefield extrapolators

ABSTRACTSeismic imaging is an important step for imaging the subsurface structures of the Earth. One of the attractive domains for seismic imaging is explicit frequency–space (f – x) prestack depth migration. So far, this domain focused on migrating seismic data in acoustic media, but very little work assumed visco‐acoustic media. In reality, seismic exploration data amplitudes suffer from attenuation. To tackle the problem of attenuation, new operators are required, which compensates for it. We propose the weighted L1‐error minimisation technique to design visco‐acoustic f – x wavefield extrapolators. The L1‐error wavenumber responses provide superior extrapolator designs as compared with the previously designed equiripple L4‐norm and L∞‐norm extrapolation wavenumber responses. To verify the new compensating designs, prestack depth migration is performed on the challenging Marmousi model dataset. A reference migrated section is obtained using non‐compensating f – x extrapolators on an acoustic dataset. Then, both compensating and non‐compensating extrapolators are applied to a visco‐acoustic dataset, and both migrated sections are then compared. The final images show that the proposed weighted L1‐error method enhances the resolution and results in practically stable images.

SUPERRESOLUTION SAR IMAGING ALGORITHM BASED ON MVM AND WEIGHTED NORM EXTRAPOLATION

Abstract. In this paper, we present an extrapolation approach, which uses minimum weighted norm constraint and minimum variance spectrum estimation, for improving synthetic aperture radar (SAR) resolution. Minimum variance method is a robust high resolution method to estimate spectrum. Based on the theory of SAR imaging, the signal model of SAR imagery is analyzed to be feasible for using data extrapolation methods to improve the resolution of SAR image. The method is used to extrapolate the efficient bandwidth in phase history field and better results are obtained compared with adaptive weighted norm extrapolation (AWNE) method and traditional imaging method using simulated data and actual measured data.

Hologram pressure field weighted norm extrapolation method

Hologram pressure extrapolation is a key process of Patch nearfield acoustic holography. In this paper, a pressure extrapolation method based on the weighted norm extrapolation is proposed, in which the auto-power spectrum of hologram pressure estimated by the measured data is used as a weighting function to build the weighted norm, and the hologram pressure extrapolation is realized by minimizing the weighted norm. Because both the spectrum shape and the bandwidth information about hologram pressure are utilized during the extrapolation, a more accurate extrapolated result can be achieved. The validity of the proposed method is proved by a numerical simulation and an experiment study. The simulation results show that the proposed method has obviously advantages in accurcy and computational efficiency compared with the classic wave number domain extrapolation method. Furthermore, the proposed method is used to extrapolate a pressure field generated by a camped steel plate in experiment and a satisfactory result is obtained.

SAR Image Superresolution via 2-D Adaptive Extrapolation

In this paper, we present a description of a nonparametric two dimensional (2-D) procedure to extrapolate a signal, an extension of the Adaptive Weighted Norm Extrapolation (AWNE) method, and illustrate its application to SAR image formation. The benefits of the AWNE procedure are shown for synthetic data and for MSTAR data. Once the phase history is recovered, the AWNE method is applied to a subaperture or to the full set of frequency samples to extrapolate them to a larger aperture from which a superresolved complex SAR image is obtained. Use of the 2-D AWNE procedure proves to be superior to its one-dimensional separable version by reducing undesirable effects such as sidelobe interference, and variability in energy of the extrapolated data from row to row and column to column. To assess the performance of AWNE in enhancing prominent scatterers, reducing speckle, and suppressing clutter, we compare the superresolved images to the images formed with the traditional Fourier technique starting from the same phase history data. Fourier images are also compared with superresolved images formed using less data in order to assess the quality of the extrapolation and to quantify the method's ability to recover lost resolution. We illustrate performance by visual comparison and by the use of a geometric constellation of prominent point scatterers of the targets extracted from the images. A brief comparison with the 2-D versions of Capon and the linear prediction methods is illustrated and a hybrid AWNE/Capon approach is proposed.