No abstract

Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichmüller maps are used for establishing a 1-1 correspondence between tooth surfaces with prescribed anatomical landmarks. Then, a quasi-conformal statistical shape analysis model based on the Teichmüller mapping results is proposed for building a tooth classification scheme. We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries.Experimental results show that our method achieves much higher classification accuracy with respect to both gender and ancestry when compared to the existing methods. Furthermore, our model reveals the underlying tooth shape difference between different genders and ancestries in terms of the local * Corresponding author.

Singular spectrum analysis (SSA) or Cadzow reduced-rank filtering is an efficient method for random noise attenuation. SSA starts by embedding the seismic data into a Hankel matrix. Rank reduction of this Hankel matrix followed by antidiagonal averaging is utilized to estimate an enhanced seismic signal. Rank reduction is often implemented via the singular value decomposition (SVD). The SVD is a nonrobust matrix factorization technique that leads to suboptimal results when the seismic data are contaminated by erratic noise. The term erratic noise designates non-Gaussian noise that consists of large isolated events with known or unknown distribution. We adopted a robust low-rank factorization that permitted use of the SSA filter in situations in which the data were contaminated by erratic noise. In our robust SSA method, we replaced the quadratic error criterion function that yielded the truncated SVD solution by a bisquare function. The Hankel matrix was then approximated by the product of two lower dimensional factor matrices. The iteratively reweighed least-squares method was used to approximately solve for the optimal robust factorization. Our algorithm was tested with synthetic and real data. In our synthetic examples, the data were contaminated with band-limited Gaussian noise and erratic noise. Then, denoising was carried out by means of f-x deconvolution, the classical SSA method, and the proposed robust SSA method. The f-x deconvolution and the classical SSA method failed to properly eliminate the noise and to preserve the desired signal. On the other hand, the robust SSA method was found to be immune to erratic noise and was able to preserve the desired signal. We also tested the robust SSA method with a data set from the Western Canadian Sedimentary Basin. The results with this data set revealed improved denoising performance in portions of data contaminated with erratic noise.

Purpose Although international product-harm crises have become more common, the influence of the country image (CI) associated with foreign goods in such crises remains under researched. This study aims to investigate the extent to which the CI of a foreign made product influences consumers’ attribution of blame and trust and, ultimately, their future purchase intentions after the product is involved in a crisis. Design/methodology/approach A 2 (country) × 3 (crisis type) quasi experimental design was used, with data collected from Australia (n = 375) and China (n = 401). Findings CI can influence attribution of blame, subsequent levels of trust and likely purchase intentions. Australian and Chinese consumers have different views when it comes to trusting a company or placing blame, depending on the country of origin or the type of crisis. The direct and positive effect of CI on consumer purchase intentions following a product-harm crisis is sequentially mediated by attribution of blame and trust. Trust is the most powerful influence on future purchase intentions in both samples. Research limitations/implications In this research, only one type of crisis response strategy (no comment) was used. Thus, the results of this study must be viewed with caution when considering outcomes relating to other response options. Additionally, the testing was limited to only two samples, focussing on three countries (England, China, Vietnam), and one product context using a hypothetical brand. Further, despite our reasonable sample size (N = 776), the number of respondents represented in each cell would still be considered a limitation overall. Practical implications When developing crisis response strategies, managers should take into account the influence of a positive/negative source CI in driving attribution and trust. To minimize the impact of crisis on future purchasing decisions, organizations can leverage positive biases and mitigate negative ones, aiming to maintain or restore trust as a priority. Originality/value The study provides cross-country understanding about the significant role of CI during a product-harm crisis in relation to subsequent consumers’ blame attribution, their trust in the focal organization and ultimately their future purchase intentions.

Time-domain elastic least-squares reverse time migration (LSRTM) is formulated as a linearized elastic full-waveform inversion problem. The elastic Born approximation and elastic reverse time migration (RTM) operators are derived from the time-domain continuous adjoint-state method. Our approach defines P- and S-wave impedance perturbations as unknown elastic images. Our algorithm is obtained using continuous functional analysis in which the problem is discretized at the final stage (optimize-before-discretize approach). The discretized numerical versions of the elastic Born operator and its adjoint (elastic RTM operator) pass the dot-product test. The conjugate gradient least-squares method is used to solve the least-squares migration quadratic optimization problem. In other words, the Hessian operator for elastic LSRTM is implicitly inverted via a matrix-free algorithm that only requires the action of forward and adjoint operators on vectors. The diagonal of the pseudo-Hessian operator is used to design a preconditioning operator to accelerate the convergence of the elastic LSRTM. The elastic LSRTM provides higher resolution images with fewer artifacts and a superior balance of amplitudes when compared with elastic RTM. More important, elastic LSRTM can mitigate crosstalk between the P- and S-wave impedance perturbations given that the off-diagonal elements of the Hessian are attenuated via the inversion.