No Arabic abstract
For a long time, RBS and PIXE techniques have been used in the field of cultural heritage. Although the complementarity of both techniques has long been acknowledged, its full potential has not been yet developed due to the lack of general purpose software tools for analysing the data from both techniques in a coherent way. In this work we provide an example of how the recent addition of PIXE to the set of techniques supported by the DataFurnace code can significantly change this situation. We present a case in which a non homogeneous sample (an oxidized metal from a photographic plate -heliography- made by Niepce in 1827) is analysed using RBS and PIXE in a straightforward and powerful way that can only be performed with a code that treats both techniques simultaneously as a part of one single and coherent analysis. The optimization capabilities of DataFurnace, allowed us to obtain the composition profiles for these samples in a very simple way.
It is proposed in this study to observe the influence of P2O5 on the formation of the apatite-like layer in a bioactive glass via a complete PIXE characterization. A glass in the SiO2-CaO-P2O5 ternary system was elaborated by sol-gel processing. Glass samples were soaked in biological fluids for periods up to 10 days. The surface changes were characterized using Particle Induced X-ray Emission (PIXE) associated to Rutherford Backscattering Spectroscopy (RBS), which are efficient methods for multielemental analysis. Elemental maps of major and trace elements were obtained at a micrometer scale and revealed the bone bonding ability of the material. The formation of a calcium phosphate-rich layer containing magnesium occurs after a few days of interaction. We demonstrate that the presence of phosphorus in the material has an impact on the development and the formation rate of the bone-like apatite layer. Indeed, the Ca/P atomic ratio at the glass/biological fluids interface is closer to the nominal value of pure apatite compared to P2O5 free glasses. It would permit, in vivo, an improved chemical bond between the biomaterials and bone.
The surfaces of many cultural heritage objects were embellished with various patterns, especially curve patterns. In practice, most of the unearthed cultural heritage objects are highly fragmented, e.g., sherds of potteries or vessels, and each of them only shows a very small portion of the underlying full design, with noise and deformations. The goal of this paper is to address the challenging problem of automatically identifying the underlying full design of curve patterns from such a sherd. Specifically, we formulate this problem as template matching: curve structure segmented from the sherd is matched to each location with each possible orientation of each known full design. In this paper, we propose a new two-stage matching algorithm, with a different matching cost in each stage. In Stage 1, we use a traditional template matching, which is highly computationally efficient, over the whole search space and identify a small set of candidate matchings. In Stage 2, we derive a new matching cost by training a dual-source Convolutional Neural Network (CNN) and apply it to re-rank the candidate matchings identified in Stage 1. We collect 600 pottery sherds with 98 full designs from the Woodland Period in Southeastern North America for experiments and the performance of the proposed algorithm is very competitive.
Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map. Different from most classical low-level image segmentation methods that are known to be very sensitive to the noise and occlusions, we propose a new supervised learning algorithm based on Convolutional Neural Network (CNN) to implicitly learn and utilize more curve geometry and pattern information for addressing this challenging problem. More specifically, we first propose a Fully Convolutional Network (FCN) to estimate the skeleton of curve structures and at each skeleton pixel, a scale value is estimated to reflect the local curve width. Then we propose a dense prediction network to refine the estimated curve skeletons. Based on the estimated scale values, we finally develop an adaptive thresholding algorithm to achieve the final segmentation of curve structures. In the experiment, we validate the performance of the proposed method on a dataset of depth images scanned from unearthed pottery sherds dating to the Woodland period of Southeastern North America.
Pt/Ti metallisation bilayers are used as bottom electrodes for ferroelectric thin films. During deposition of the ferroelectric films, these electrodes are exposed to elevated temperatures causing modifications of the Pt/Ti bottom electrode. Diffusion and oxidation of the Ti adhesion layer have been studied by the application of factor analysis to AES depth profile data and by RBS. Factor analysis was employed to extract the chemical information from the measured AES spectra and to derive semiquantitative depth profiles of the identified material compounds. RBS was used to obtain the quantitative depth distribution of the elements. By the combination of both methods, diffusion and oxidation processes were observed and could be precisely describe.
Individual performance metrics are commonly used to compare players from different eras. However, such cross-era comparison is often biased due to significant changes in success factors underlying player achievement rates (e.g. performance enhancing drugs and modern training regimens). Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multi-billion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics -- despite the fact that many player careers span different eras. Remarkably, we find that the method applied to comprehensive Major League Baseball and National Basketball Association player data preserves the overall functional form of the distribution of career achievement, both at the season and career level. As such, subsequent re-ranking of the top-50 all-time records in MLB and the NBA using renormalized metrics indicates reordering at the local rank level, as opposed to bulk reordering by era. This local order refinement signals time-independent mechanisms underlying annual and career achievement in professional sports, meaning that appropriately renormalized achievement metrics can be used to compare players from eras with different season lengths, team strategies, rules -- and possibly even different sports.