Quantifying Object Similarity: Applying Locality Sensitive Hashing for Comparing Material Culture

We present a novel technique that compares and quantifies images used here to compare similarities between material cultures. This method is based on locality sensitive hashing (LSH), which uses a relatively fast and flexible algorithm to compare image data and determine their level of similarity. This technique is applied to a dataset of sculpture faces from the Aegean, Anatolia, Cyprus, Egypt, Iran, Indus/Gandhara, the Levant, and Mesopotamia. Results indicate that the objects can be differentiated based on regional differences and show similarities to other locations that share specific material culture traits. Images from known locations enable a network of compared objects to be constructed, where inverse closeness centrality and link weights are used to indicate areas that have a greater or less cultural similarity to other regions. Different periods are assessed, and the results demonstrate that objects from earlier than the 9th century BCE show greater similarity to other local and Egyptian items. Objects from between the 9th and 4th centuries BCE increasingly show inter-regional similarity,with the eastern Mediterranean, including the Aegean, Anatolia, Egypt, and Cyprus,having close similarity to multiple regions. After the 4 th century BCE, greater sculptural similarity is found across a wide area, including the Aegean, Cyprus, Egypt,Mesopotamia, and Gandhara. In general, sculptures from more distant areas increase in similarity in later periods, that is starting from the 9th century BCE. The results demonstrate that the technique can be applied to quantifying object similarity and extended to a broad range of archaeological objects, while also being a tool for rapid analysis that requires minimal data compared to some machine learning techniques.The code and data are provided as part of the outputs

The structure, centrality, and scale of urban street networks: Cases from Pre-Industrial Afro-Eurasia

Cities and towns have often developed infrastructure that enabled a variety of socio-economic interactions. Street networks within these urban settings provide key access to resources, neighborhoods, and cultural facilities. Studies on settlement scaling have also demonstrated that a variety of urban infrastructure and resources indicate clear population scaling relationships in both modern and ancient settings. This article presents an approach that investigates past street network centrality and its relationship to population scaling in urban contexts. Centrality results are compared statistically among different urban settings, which are categorized as orthogonal (i.e., planned) or self-organizing (i.e., organic) urban settings, with places having both characteristics classified as hybrid. Results demonstrate that street nodes have a power law relationship to urban area, where the number of nodes increases and node density decreases in a sub-linear manner for larger sites. Most median centrality values decrease in a negative sub-linear manner as sites are larger, with organic and hybrid urban sites’ centrality being generally less and diminishing more rapidly than orthogonal settings. Diminishing centrality shows comparability to modern urban systems, where larger urban districts may restrict overall interaction due to increasing transport costs over wider areas. Centrality results indicate that scaling results have multiples of approximately ⅙ or ⅓ that are comparable to other urban and road infrastructure, suggesting a potential relationship between different infrastructure features and population in urban centers. The results have implications for archaeological settlements where urban street plans are incomplete or undetermined, as it allows forecasts to be made on past urban sites’ street network centrality. Additionally, a tool to enable analysis of street networks and centrality is provided as part of the contribution

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs given

The upper critical field of filamentary Nb3Sn conductors

We have examined the upper critical field of a large and representative set of present multi-filamentary Nb3Sn wires and one bulk sample over a temperature range from 1.4 K up to the zero field critical temperature. Since all present wires use a solid-state diffusion reaction to form the A15 layers, inhomogeneities with respect to Sn content are inevitable, in contrast to some previously studied homogeneous samples. Our study emphasizes the effects that these inevitable inhomogeneities have on the field-temperature phase boundary. The property inhomogeneities are extracted from field-dependent resistive transitions which we find broaden with increasing inhomogeneity. The upper 90-99 % of the transitions clearly separates alloyed and binary wires but a pure, Cu-free binary bulk sample also exhibits a zero temperature critical field that is comparable to the ternary wires. The highest mu0Hc2 detected in the ternary wires are remarkably constant: The highest zero temperature upper critical fields and zero field critical temperatures fall within 29.5 +/- 0.3 T and 17.8 +/- 0.3 K respectively, independent of the wire layout. The complete field-temperature phase boundary can be described very well with the relatively simple Maki-DeGennes model using a two parameter fit, independent of composition, strain state, sample layout or applied critical state criterion.Comment: Accepted Journal of Applied Physics Few changes to shorten document, replaced eq. 7-

Strongly linked current flow in polycrystalline forms of the new superconductor MgB2

The discovery of superconductivity at 39 K in MgB2[1] raises many issues. One of the central questions is whether this new superconductor resembles a high-temperature-cuprate superconductor or a low-temperature metallic superconductor in terms of its current carrying characteristics in applied magnetic fields. In spite of the very high transition temperatures of the cuprate superconductors, their performance in magnetic fields has several drawbacks[2]. Their large anisotropy restricts high bulk current densities to much less than the full magnetic field-temperature (H-T) space over which superconductivity is found. Further, weak coupling across grain boundaries makes transport current densities in untextured polycrystalline forms low and strongly magnetic field sensitive[3,4]. These studies of MgB2 address both issues. In spite of the multi-phase, untextured, nano-scale sub-divided nature of our samples, supercurrents flow throughout without the strong sensitivity to weak magnetic fields characteristic of Josephson-coupled grains[3]. Magnetization measurements over nearly all of the superconducting H-T plane show good temperature scaling of the flux pinning force, suggestive of a current density determined by flux pinning. At least two length scales are suggested by the magnetization and magneto optical (MO) analysis but the cause of this seems to be phase inhomogeneity, porosity, and minority insulating phase such as MgO rather than by weakly coupled grain boundaries. Our results suggest that polycrystalline ceramics of this new class of superconductor will not be compromised by the weak link problems of the high temperature superconductors, a conclusion with enormous significance for applications if higher temperature analogs of this compound can be discovered