Modeling Strategic Decisions in the Formation of the Early Neo-Assyrian Empire

Understanding patterns of conflict and pathways in which political history became established is critical to understanding how large states and empires ultimately develop and come to rule given regions and influence subsequent events. We employ a spatiotemporal Cox regression model to investigate possible causes as to why regions were attacked by the Neo-Assyrian (912-608 BCE) state. The model helps to explain how strategic benefits and costs lead to likely pathways of conflict and imperialism based on elite strategic decision-making. We apply this model to the early 9th century BCE, a time when historical texts allow us to trace yearly campaigns in specific regions, to understand how the Neo-Assyrian state began to re-emerge as a major political player, eventually going on to dominate much of the Near East and starting a process of imperialism that shaped the wider region for many centuries even after the fall of this state. The model demonstrates why specific locations become regions of conflict in given campaigns, emphasizing a degree of consistency with which choices were made by invading forces with respect to a number of factors. We find that elevation and population density deter Assyrian invasions. Moreover, costs were found to be more of a clear motivator for Assyrian invasions, with distance constraints being a significant driver in determining where to campaign. These outputs suggest that Assyria was mainly interested in attacking its weakest, based on population and/or organization, and nearest rivals as it began to expand. Results not only help to address the emergence of this empire, but enable a generalized understanding of how benefits and costs to conflict can lead to imperialism and pathways to political outcomes that can have major social relevance

The Effect of Choice of IPad-Delivered Math Independent Practice of Elementary Grade Students With Attention-Deficit/Hyperactivity Characteristics

A choice-making strategy is an antecedent control that has proven to be effective for students with problem behaviors. Because students with Attention-deficit/Hyperactivity Disorder (ADHD) may display disruptive behaviors and show poor academic performance, it has been suggested that incorporating choice-making strategies into academic instruction could serve to increase academic engagement and task accuracy. The purpose of this study was to examine the effectiveness of iPad-based choice-making opportunities during math independent practice on each participant\u27s task engagement, time required to complete task, task accuracy, and task completion, as well as the teacher and participants perceptions of social validity of the intervention. A single-subject reversal design ABAB and its counterbalancing BABA design were used to examine the effects of iPad-based choices during independent work time on math performance and behavioral responses of four participants. Visual analysis and two non-parametric overlap methods (i.e., percent of non-overlapping data [PND] and percent of data points exceeding the median line [PEM]) were employed to determine treatment effect on each dependent variable and for each participant. The results of this study were mixed. As evidenced by overall PND and/or PEM calculation estimates, there was an effect of the intervention on: (a) task engagement for Participant One, Participant Two, and Participant Four; (b) time required to complete task for all four participants; and (c) task accuracy for Participant One and Participant Three. No functional relation was established between the intervention and participants task completion. The teacher and three participants reported that the intervention was socially valid on most of the items in the social validity assessments. Potential explanations of the reported results, study limitations, and implications for future research are discussed

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

Toward a typology for social-ecological systems

Characterizing and understanding social-ecological systems (SESs) is increasingly necessary to answer questions about the development of sustainable human settlements. To date, much of the literature on SES analysis has focused on "neat" systems involving a single type of resource, a group of users, and a governance system. While these studies provide valuable and specific insights, they are of limited use for application to "messy" SESs that encompass the totality of human settlements, including social organization and technologies that result in the movement of materials, energy, water, and people. These considerations, in turn, create distribution systems that lead to different types of SESs. In messy SESs the concept of resilience, or the ability of a system to withstand perturbation while maintaining function, is further evolved to posit that different settlements will require different approaches to foster resilience. This article introduces a typology for refining SESs to improve short- and long-term adaptive strategies in developing human settlements

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

Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites

Constant detection and monitoring of archaeological sites and objects have always been an important national goal for many countries. The early identification of changes is crucial to preventive conservation. Archaeologists have always considered using service drones to automate collecting data on and below the ground surface of archaeological sites, with cost and technical barriers being the main hurdles against the wide-scale deployment. Advances in thermal imaging, depth imaging, drones, and artificial intelligence have driven the cost down and improved the quality and volume of data collected and processed. This paper proposes an end-to-end framework for archaeological sites detection and monitoring using autonomous service drones. We mount RGB, depth, and thermal cameras on an autonomous drone for low-altitude data acquisition. To align and aggregate collected images, we propose two-stage multimodal depth-to-RGB and thermal-to-RGB mosaicking algorithms. We then apply detection algorithms to the stitched images to identify change regions and design a user interface to monitor these regions over time. Our results show we can create overlays of aligned thermal and depth data on RGB mosaics of archaeological sites. We tested our change detection algorithm and found it has a root mean square error of 0.04. To validate the proposed framework, we tested our thermal image stitching pipeline against state-of-the-art commercial software. We cost-effectively replicated its functionality while adding a new depth-based modality and created a user interface for temporally monitoring changes in multimodal views of archaeological sites

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