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- Agia marina and Peristereònas: two new epipalaeolithic sites on the Island of Lemnos (Greece)Publication . Efstratiou, Nikos; Biagi, Paolo; Starnini, Elisabetta; Kyriakou, Dimitris; ELEFTHERIADOU, ANASTASIAThe surveys carried out along the coasts of the island of Lemnos (Greece) have led to the discovery of new Late Epipalaeolithic sites at Agia Marina and Peristereonas. Peristereonas yielded a knapped stone assemblage that is strictly comparable with that from Ouriakos, a site located along the south-eastern coast of the same island, while the artefacts from Agia Marina are more problematic to interpret because they are probably to be attributed to a slightly different period. However, the most characteristic artefacts recovered from the sites are represented by microlithic geometrics obtained by abrupt, bipolar, or direct retouch, end scrapers, and different types of exhausted cores and technical pieces, which help us reconstruct the operational sequence employed for the manufacture of the armatures. The aim of the paper is to contribute to the interpretation of the characteristics of the Late Epipalaeolithic assemblages discovered on the island and to frame them into the general picture of the end of the Pleistocene in this part of the Aegean. The artefacts from the sites show unique characteristics, without parallels to the knapped stone assemblages of the same period so far recovered along the coasts of the Aegean Sea, the eastern Mediterranean, the Levant, and the Black Sea.
- Correction to: Agia Marina and Peristereònas: Two New Epipalaeolithic Sites on the Island of Lemnos (Greece)Publication . Efstratiou, Nikos; Biagi, Paolo; Starnini, Elisabetta; Kyriakou, Dimitris; ELEFTHERIADOU, ANASTASIAThe original version of this article unfortunately contained a mistake. The 5th afliation should be Interdisciplinary Center for Archaeology and Evolution of Human Behavior (ICArEHB), Universidade do Algarve, Faro, Portugal. The original article has been corrected.
- Classifying polish in use-wear analysis with convolutional neural networksPublication . Eleftheriadou, Anastasia; Djellal, Youssef; McPherron, Shannon; Marreiros, JoaoLithic use-wear analysis examines micro- and macroscopic traces on tool surfaces resulting from human use and post-depositional processes. Polish, formed through surface abrasion with different materials, is a key diagnostic feature that is increasingly analyzed using machine learning to enhance automation and standardization. However, further research is needed to explore whether deep learning approaches, in particular, can be effectively applied to use-wear analysis and to determine the optimal surface area size (e.g., patch size and microscope objectives) and model architecture (custom vs. pre-trained) for achieving the best results. This study employs convolutional neural networks (CNNs) to classify experimental polish based on contact material (wood, hide, bone) and use intensity, while also assessing optimal imaging and analytical parameters. The results of this exploratory study suggest that CNNs may effectively identify polish from bone and hide but perform less effectively with wood. The models also successfully distinguish between polish formed by short- and long-term use. Custom models outperformed pre-trained ones, particularly when using images that captured smaller areas of the tool’s surface, suggesting that bigger surface areas may lack the necessary information for optimal results. These findings underscore the need to expand use-wear datasets in terms of size and variability and optimize CNN architectures and workflows.
- Machine learning applications in use-wear analysis: a critical reviewPublication . Eleftheriadou, Anastasia; McPherron, Shannon P.; Marreiros, JoãoUse-wear analysis examines the macroscopic and microscopic patterns of traces left on tool surfaces as a result of use. Recently, machine learning (ML) has been employed as a promising method for automating and standardizing the identification of these traces. While the number of use-wear analysts using ML continues to grow, discussions regarding the effectiveness and appropriate implementation of these methods are ongoing. The main aim of this literature review is to provide recommendations for the more effective application of ML in use-wear analysis and archaeological research, by identifying trends, research gaps, and evaluating the quality of the models developed. There are three key challenges identified. Firstly, the limited adoption of open science practices restricts the creation of large datasets and hinders reproducibility and transparency. Secondly, research efforts are concentrated within limited institutions, focusing on certain research questions, algorithms, raw materials, and use-wear traces. Thirdly, the inadequate quality, quantity, and diversity of data affect the performance of the models being developed. To address these challenges, this paper advocates for the promotion of open science and the systematic gathering of experimental and analytical data. Involving a broader range of institutions can improve research quality and promote greater diversity of perspectives. Collaboration with computer scientists and computational archaeologists is essential to integrate the expertise necessary for designing and implementing effective ML methods. By addressing these factors, this paper facilitates the effective use of machine learning, enabling use-wear analysts and archaeologists to develop robust models that automate, accelerate, and improve their research.