Hasil untuk "Archaeology"

Ziyao Ling, Silvia Mirri, Paola Salomoni et al.

The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porcelain. This study investigates whether synthetic images generated through Stable Diffusion with Low-Rank Adaptation (LoRA) can effectively augment limited real datasets for multi-task CNN-based porcelain classification. Using MobileNetV3 with transfer learning, we conducted controlled experiments comparing models trained on pure real data against those trained on mixed real-synthetic datasets (95:5 and 90:10 ratios) across four classification tasks: dynasty, glaze, kiln and type identification. Results demonstrate task-specific benefits: type classification showed the most substantial improvement (5.5\% F1-macro increase with 90:10 ratio), while dynasty and kiln tasks exhibited modest gains (3-4\%), suggesting that synthetic augmentation effectiveness depends on the alignment between generated features and task-relevant visual signatures. Our work contributes practical guidelines for deploying generative AI in archaeological research, demonstrating both the potential and limitations of synthetic data when archaeological authenticity must be balanced with data diversity.

Adeela Islam, Stefano Fiorini, Stuart James et al.

The task of reassembly is a significant challenge across multiple domains, including archaeology, genomics, and molecular docking, requiring the precise placement and orientation of elements to reconstruct an original structure. In this work, we address key limitations in state-of-the-art Deep Learning methods for reassembly, namely i) scalability; ii) multimodality; and iii) real-world applicability: beyond square or simple geometric shapes, realistic and complex erosion, or other real-world problems. We propose ReassembleNet, a method that reduces complexity by representing each input piece as a set of contour keypoints and learning to select the most informative ones by Graph Neural Networks pooling inspired techniques. ReassembleNet effectively lowers computational complexity while enabling the integration of features from multiple modalities, including both geometric and texture data. Further enhanced through pretraining on a semi-synthetic dataset. We then apply diffusion-based pose estimation to recover the original structure. We improve on prior methods by 57% and 87% for RMSE Rotation and Translation, respectively.

Leonardi Melo, Luís Gustavo, Dimmy Magalhães et al.

This study presents a comparative analysis of three U-Net-based architectures for semantic segmentation of rock art petroglyphs from Brazilian archaeological sites. The investigated architectures were: (1) BEGL-UNet with Border-Enhanced Gaussian Loss function; (2) Attention-Residual BEGL-UNet, incorporating residual blocks and gated attention mechanisms; and (3) Spatial Channel Attention BEGL-UNet, which employs spatial-channel attention modules based on Convolutional Block Attention Module. All implementations employed the BEGL loss function combining binary cross-entropy with Gaussian edge enhancement. Experiments were conducted on images from the Poço da Bebidinha Archaeological Complex, Piauí, Brazil, using 5-fold cross-validation. Among the architectures, Attention-Residual BEGL-UNet achieved the best overall performance with Dice Score of 0.710, validation loss of 0.067, and highest recall of 0.854. Spatial Channel Attention BEGL-UNet obtained comparable performance with DSC of 0.707 and recall of 0.857. The baseline BEGL-UNet registered DSC of 0.690. These results demonstrate the effectiveness of attention mechanisms for archaeological heritage digital preservation, with Dice Score improvements of 2.5-2.9% over the baseline.

Ye Tao, Xinran Fu, Honglin Pang et al.

Oracle Bone Inscriptions (OBIs), play a crucial role in understanding ancient Chinese civilization. The automated detection of OBIs from rubbing images represents a fundamental yet challenging task in digital archaeology, primarily due to various degradation factors including noise and cracks that limit the effectiveness of conventional detection networks. To address these challenges, we propose a novel clustering-based feature space representation learning method. Our approach uniquely leverages the Oracle Bones Character (OBC) font library dataset as prior knowledge to enhance feature extraction in the detection network through clustering-based representation learning. The method incorporates a specialized loss function derived from clustering results to optimize feature representation, which is then integrated into the total network loss. We validate the effectiveness of our method by conducting experiments on two OBIs detection dataset using three mainstream detection frameworks: Faster R-CNN, DETR, and Sparse R-CNN. Through extensive experimentation, all frameworks demonstrate significant performance improvements.

Cheyanne Shariat, Kareem El-Badry, Soumyadeep Bhattacharjee

Precise stellar ages are fundamental to Galactic archaeology. However, obtaining reliable age estimates and uncertainties for field stars has been a long-standing challenge. We test the fidelity of ages from recent catalogs of giants and subgiants using wide binaries, whose components formed at the same time and thus should have consistent inferred ages. We find that subgiant ages based on spectroscopic metallicities from Xiang & Rix (2022) are generally consistent within their reported uncertainties, implying that fractional uncertainties of 5-10% are realistically achievable. In contrast, we find that published photometric subgiant ages underestimate true uncertainties by factors of 2-3. Spectroscopic age estimates for red giant and red clump stars also show reliable uncertainties, but are generally less precise (25-30%). These results demonstrate that accurate metallicity and $α$-element abundances are essential for precise subgiant ages and establish wide binaries as a powerful, model-independent benchmark for calibrating stellar age measurements in the era of large spectroscopic surveys.

Benjamin Jung, Johannes Bosse, Malte Göttsche

Although North Korea's nuclear program has been the subject of extensive scrutiny, estimates of its fissile material stockpiles remain fraught with uncertainty. In potential future disarmament agreements, inspectors may need to use nuclear archaeology methods to verify or gain confidence in a North Korean fissile material declaration. This study explores the potential utility of a Bayesian inference-based analysis of the isotopic composition of reprocessing waste to reconstruct the operating history of the 5 MWe reactor and estimate its plutonium production history. We simulate several scenarios that reflect different assumptions and varying levels of prior knowledge about the reactor. The results show that correct prior assumptions can be confirmed and incorrect prior information (or a false declaration) can be detected. Model comparison techniques can distinguish between scenarios with different numbers of core discharges, a capability that could provide important insights into the early stages of operation of the 5 MWe reactor. Using these techniques, a weighted plutonium estimate can be calculated, even in cases where the number of core discharges is not known with certainty.

Chelsea Thompto

Artists and especially new media artists contribute to public perceptions and adoption of new technologies through their own use of emerging media technologies such as augmented and virtual reality, generative image systems, and high-resolution displays in the production of their work. In this way, art and media production can be understood as part of the larger issue of unsustainable computational consumption. As such, it is critical for artists to develop, share, and promote new and more sustainable methods of engaging with technology, especially within the context of higher education. This paper will explore how artists might implement more sustainable methods by considering the relationship between the technical approaches of compute reuse, sustainable web development, and frugal computing, and the concepts of material specificity , futurity, and media archaeology . Proposing three methods of less carbon-intensive artistic production and a set of guidelines for introducing sustainable methods into arts and technology curriculum, this paper will outline not only the technical viability of these approaches but also the rich conceptual opportunities these approaches might offer to artists and viewers alike. For each method, models for pedagogical implementation will be explored with an emphasis on how local resources and sustainability contexts should play a role.

Alankrit Gupta, Adnan Abdullah, Xianyao Li et al.

Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to deploy this framework on a novel AUV (Autonomous Underwater Vehicle) named CavePI. The guided navigation is driven by a computationally light yet robust deep visual perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate within complex cave structures. We evaluate the system through field experiments in natural underwater caves and spring-water sites and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions.

Theodore Avgitas, Jean-Christophe Ianigro, Jacques Marteau

Muography is an investigation technique based on the detection of the atmospheric muon flux' modification through matter. It has found lately multiple applications in geosciences, archaelogy, and non invasive industrial controls. Mostly known for its imaging capabilities, muography may be exploited as well for monitoring purposes since the atmospheric muon flux is available permanently. In this paper we present an interesting measurement performed in the context of an archaelogical project called Archémuons, on the archaeological site of "Palais du Miroir" in Vienne, South of Lyon, France. We installed a muon detector in an underground gallery within the foundations of the building for the second half of 2023. The primary goal is to measure details of those foundations which are largely not excavated yet. Meanwhile we observed over more than 6 months long-term and short-term variations of the muon rates since the start of the experiment, which seem to exhibit a correlation with the rain accumulating on the free field just above the gallery. We propose as an explanation for this behavior the retention of water by the soil above the detector site.

Nicole Buckley, Payel Das, Paula Jofré et al.

We present a detailed study of the chemical diversity of the metal-poor Milky Way (MW) using data from the GALAH DR3 survey. Considering 17 chemical abundances relative to iron ([X/Fe]) for 9,923 stars, we employ Principal Component Analysis (PCA) and Extreme Deconvolution (XD) to identify 10 distinct stellar groups. This approach, free from chemical or dynamical cuts, reveals known populations, including the accreted halo, thick disc, thin disc, and in-situ halo. The thick disc is characterised by multiple substructures, suggesting it comprises stars formed in diverse environments. Our findings highlight the limited discriminatory power of magnesium in separating accreted and disc stars. Elements such as Ba, Al, Cu, and Sc are critical in distinguishing disc from accreted stars, while Ba, Y, Eu and Zn differentiate disc and accreted stars from the in-situ halo. This study demonstrates the potential power of combining a latent space representation of the data (PCA) with a clustering algorithm (XD) in Galactic archaeology, in providing new insights into the galaxy's assembly and evolutionary history.

Advaith V. Sethuraman, Philip Baldoni, Katherine A. Skinner et al.

Autonomous underwater vehicles often perform surveys that capture multiple views of targets in order to provide more information for human operators or automatic target recognition algorithms. In this work, we address the problem of choosing the most informative views that minimize survey time while maximizing classifier accuracy. We introduce a novel active perception framework for multi-view adaptive surveying and reacquisition using side scan sonar imagery. Our framework addresses this challenge by using a graph formulation for the adaptive survey task. We then use Graph Neural Networks (GNNs) to both classify acquired sonar views and to choose the next best view based on the collected data. We evaluate our method using simulated surveys in a high-fidelity side scan sonar simulator. Our results demonstrate that our approach is able to surpass the state-of-the-art in classification accuracy and survey efficiency. This framework is a promising approach for more efficient autonomous missions involving side scan sonar, such as underwater exploration, marine archaeology, and environmental monitoring.

Giedrė Motuzaitė Matuzevičiūtė, Rimvydas Laužikas

The eastern Baltic region represents the world’s most northerly limit of successful broomcorn millet (<i>Panicum miliaceum</i>) (hereafter, millet) cultivation in the past, yet this crop has been almost forgotten today. The earliest millet in the eastern Baltic region has been identified from macrobotanical remains which were directly dated to ca 1000 BCE. Between 800 and 500 BCE, millet was one of the major staple foods in the territory of modern-day Lithuania. Millet continued to play an important role in past agriculture up until the 15th century, with its use significantly declining during the following centuries. This paper analyses both the archaeobotanical records and written sources on broomcorn millet cultivation in Lithuania from its first arrival all the way through to the 19th century. The manuscript reviews the evidence of millet cultivation in the past as documented by archaeobotanical remains and historical accounts. In light of fluctuating records of millet cultivation through time, we present the hypothetical reasons for the decline in millet use as human food. The paper hypothesizes that the significant decrease in broomcorn millet cultivation in Lithuania from the 15th century onwards was likely influenced by several factors, which include climate change (the Little Ice Age) and the agricultural reforms of the 16th century. However, more detailed research is required to link past fluctuations in millet cultivation with climatic and historical sources, thus better understanding the roots of collapsing crop biodiversity in the past.

Anibal Alviz-Meza, Manuel H. Vásquez-Coronado, Jorge G. Delgado-Caramutti et al.

Abstract Using past material and spiritual remains, cultural heritage examines communities’ identity formation across time. Cultural heritage requires public and private institutions to care about its restoration, maintenance, conservation, and promotion. Through a bibliometric perspective, this study has analyzed, quantified, and mapped the scientific production of the fourth industrial revolution applied to heritage studies from 2016 to 2021 in the Scopus and Web of Science databases. Biblioshiny software from RStudio was employed to categorize and evaluate the contribution of authors, countries, institutions, and journals. In addition, VOSviewer was used to visualize their collaboration networks. As a main result, we found that augmented reality and remote sensing represent the research hotspot concerning heritage studies. Those techniques have become common in archaeology, as well as museums, leading to an increase in their activity. Perhaps, more recent tools, such as machine learning and deep learning, will provide future pathways in cultural heritage from data collected in social networks. This bibliometric analysis, therefore, provides an updated perspective of the implementations of technologies from industry 4.0 in heritage science as a possible guideline for future worldwide research.

Jiepeng Wang, Congyi Zhang, Peng Wang et al.

3D reconstruction techniques have widely been used for digital documentation of archaeological fragments. However, efficient digital capture of fragments remains as a challenge. In this work, we aim to develop a portable, high-throughput, and accurate reconstruction system for efficient digitization of fragments excavated in archaeological sites. To realize high-throughput digitization of large numbers of objects, an effective strategy is to perform scanning and reconstruction in batches. However, effective batch-based scanning and reconstruction face two key challenges: 1) how to correlate partial scans of the same object from multiple batch scans, and 2) how to register and reconstruct complete models from partial scans that exhibit only small overlaps. To tackle these two challenges, we develop a new batch-based matching algorithm that pairs the front and back sides of the fragments, and a new Bilateral Boundary ICP algorithm that can register partial scans sharing very narrow overlapping regions. Extensive validation in labs and testing in excavation sites demonstrate that these designs enable efficient batch-based scanning for fragments. We show that such a batch-based scanning and reconstruction pipeline can have immediate applications on digitizing sherds in archaeological excavations. Our project page: https://jiepengwang.github.io/FIRES/.

C. J. Riseley, E. Bonnassieux, T. Vernstrom et al.

Abell 3266 is a massive and complex merging galaxy cluster that exhibits significant substructure. We present new, highly sensitive radio continuum observations of Abell 3266 performed with the Australian Square Kilometre Array Pathfinder (0.8$-$1.1 GHz) and the Australia Telescope Compact Array (1.1$-$3.1 GHz). These deep observations provide new insights into recently-reported diffuse non-thermal phenomena associated with the intracluster medium, including a 'wrong-way' relic, a fossil plasma source, and an as-yet unclassified central diffuse ridge, which we reveal comprises the brightest part of a large-scale radio halo detected here for the first time. The 'wrong-way' relic is highly atypical of its kind: it exhibits many classical signatures of a shock-related radio relic, while at the same time exhibiting strong spectral steepening. While radio relics are generally consistent with a quasi-stationary shock scenario, the 'wrong-way' relic is not. We study the spectral properties of the fossil plasma source; it exhibits an ultra-steep and highly curved radio spectrum, indicating an extremely aged electron population. The larger-scale radio halo fills much of the cluster centre, and presents a strong connection between the thermal and non-thermal components of the intracluster medium, along with evidence of substructure. Whether the central diffuse ridge is simply a brighter component of the halo, or a mini-halo, remains an open question. Finally, we study the morphological and spectral properties of the multiple complex radio galaxies in this cluster in unprecedented detail, tracing their evolutionary history.