Anna Anguissola teaches classical archaeology at the University of Pisa. Her principal research on Greco-Roman visual, material and literary culture has focused on urban development, the relationship between Greek and Roman art, the history and techniques of ancient sculpture, the Greek and Latin literary sources on the figural arts and the reception of classical art in later periods. She is the author of Supports in Roman Marble Sculpture: Workshop Practice and Modes of Viewing (Cambridge 2018), Difficillima imitatio. Immagine e lessico delle copie tra Grecia e Roma (Rome 2012) and Intimità a Pompei: Riservatezza, condivisione e prestigio negli ambienti ad alcova di Pompei (Berlin 2010). She coordinates the University of Pisa’s field research in Pompeii’s Regio II and in the northern and southwestern burial grounds of Hierapolis in Phrygia.
Marleen Martens, Jan Bastiaens, Koen Deforce
et al.
During a rescue excavation in the (former) outskirts of the Roman small town or vicus of Elewijt (community of Zemst, Belgium), a deposit of copper alloy bowls was discovered containing plant remains preserved through contact with copper oxides originating from the corrosion of the artefacts. The most recent vessel was produced in the 3rd c. CE. An inscription on the bottom of one of the bowls mentions three names, possibly members of a family who deposited the vessels. The relevance of this ensemble for the understanding of private ritual practice in Roman Gallia is first evaluated by reflecting on the use of these copper alloy vessels prior to the deposition. Second, the botanical remains are analysed to investigate whether they were simply packaging material or whether the plants were intentionally selected as a decorative or spiritual component of the deposition, possibly in the form of a garland or a crown. The proposed interpretation of the group is not entirely conclusive but undoubtedly thought-provoking.
Oracle bone inscriptions (OBIs) are the earliest known form of Chinese characters and serve as a valuable resource for research in anthropology and archaeology. However, most excavated fragments are severely degraded due to thousands of years of natural weathering, corrosion, and man-made destruction, making automatic OBI recognition extremely challenging. Previous methods either focus on pixel-level information or utilize vanilla transformers for glyph-based OBI denoising, which leads to tremendous computational overhead. Therefore, this paper proposes a fast attentive denoising framework for oracle bone inscriptions, i.e., OBIFormer. It leverages channel-wise self-attention, glyph extraction, and selective kernel feature fusion to reconstruct denoised images precisely while being computationally efficient. Our OBIFormer achieves state-of-the-art denoising performance for PSNR and SSIM metrics on synthetic and original OBI datasets. Furthermore, comprehensive experiments on a real oracle dataset demonstrate the great potential of our OBIFormer in assisting automatic OBI recognition. The code will be made available at https://github.com/LJHolyGround/OBIFormer.
Jay Farihi, Jason L. Sanders, Sophia Lilleengen
et al.
This paper reports a Galactic kinematical and dynamical analysis of 1003 main-sequence carbon stars. The sample is drawn from the Sloan Digital Sky Survey, and cross-matched with Gaia DR3 to obtain 6-dimensional positions and velocities using a Bayesian framework. The study provides the first reliable distances for a large sample of dwarf carbon stars, which are then analyzed using both space motions and actions. The results are combined with dynamical equilibrium models for the three primary Galactic components to assign membership, finding that around 60 per cent belong to the halo, and over 30 per cent originate in the thick disc. Therefore, the results indicate dwarf carbon stars are dominated by a metal-poor halo population, and are thus an excellent resource for stellar archaeology. These stars remain on the main sequence and are relatively nearby, but atmospheric modelling is challenged by their cool effective temperatures and strong molecular features. In light of this, efforts should be made to improve C/O >1 atmospheric modelling, as the subset of low-mass dwarf carbon stars may numerically dominate the Galactic population of carbon-enriched, metal-poor stars.
Archaeological evidence shows the transformation of Sens and Troyes during Late Antiquity. The urban area shrank in the second half of the 3rd century AD and both cities had a city wall by the middle of the 4th century AD. The fortifications do not seem to constitute a clear urban limit in the 4th century AD, the distribution of finds suggesting the coexistence of extra muros settlements developing along roads exiting the city wall.Small finds from Sens suggest a strong presence of the militia intra muros, which could explain the presence of extra muros districts in the 4th and early 5th centuries AD.
In recent years, muon tomography has turned into a powerful and innovative technique for non-invasive imaging of large and small structures with applications in different areas like geology, archaeology, security, etc. We present the design and simulation of a transportable and easy to construct detector based on plastic scintillator and Silicon photomultipliers current technology. From a flux of cosmic rays reaching the atmosphere we simulated atmospheric muons at ground using CORSIKA. The detector and the object to analyze are simulated with GEANT4, where the previously obtained muon flux is transported. We use two methods for muon tomography to differentiate objects made of different materials: absorption and scattering. The statistical differences for several object sizes and materials are quantified. Using a threshold of 3 $σ$ in the first method, we conclude that materials made of lead can be differentiated from objects made of other materials. The observation time needed to differentiate an object made of lead from one of aluminum was 4.9 and 9.9 days using the first and second method, respectively. In general, the absorption method gives the best results.
Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring. Treacherous operating conditions, fragile surroundings, and limited navigation control often dictate that submersibles restrict their range of motion and, thus, the baseline over which they can capture measurements. In the context of 3D scene reconstruction, it is well-known that smaller baselines make reconstruction more challenging. Our work develops a physics-based multimodal acoustic-optical neural surface reconstruction framework (AONeuS) capable of effectively integrating high-resolution RGB measurements with low-resolution depth-resolved imaging sonar measurements. By fusing these complementary modalities, our framework can reconstruct accurate high-resolution 3D surfaces from measurements captured over heavily-restricted baselines. Through extensive simulations and in-lab experiments, we demonstrate that AONeuS dramatically outperforms recent RGB-only and sonar-only inverse-differentiable-rendering--based surface reconstruction methods. A website visualizing the results of our paper is located at this address: https://aoneus.github.io/
Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation
Geometric fracture assembly presents a challenging practical task in archaeology and 3D computer vision. Previous methods have focused solely on assembling fragments based on semantic information, which has limited the quantity of objects that can be effectively assembled. Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information. To improve the effectiveness of assembling geometric fractures without semantic information, we propose a co-creation space comprising several assemblers capable of gradually and unambiguously assembling fractures. Additionally, we introduce a novel loss function, i.e., the geometric-based collision loss, to address collision issues during the fracture assembly process and enhance the results. Our framework exhibits better performance on both PartNet and Breaking Bad datasets compared to existing state-of-the-art frameworks. Extensive experiments and quantitative comparisons demonstrate the effectiveness of our proposed framework, which features linear computational complexity, enhanced abstraction, and improved generalization. Our code is publicly available at https://github.com/Ruiyuan-Zhang/CCS.
Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life. This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces. Our approach leverages hierarchical features of global and local geometry to match and align the fracture surfaces. Our framework consists of four components: (1) front-end point feature extractor with attention layers, (2) surface segmentation to separate fracture and original parts, (3) multi-parts matching to find correspondences among fracture surface points, and (4) robust global alignment to recover the global poses of the pieces. We show how to jointly learn segmentation and matching and seamlessly integrate feature matching and rigidity constraints. We evaluate Jigsaw on the Breaking Bad dataset and achieve superior performance compared to state-of-the-art methods. Our method also generalizes well to diverse fracture modes, objects, and unseen instances. To the best of our knowledge, this is the first learning-based method designed specifically for 3D fracture assembly over multiple pieces. Our code is available at https://jiaxin-lu.github.io/Jigsaw/.
Marwa Moussawi, Andrea Giammanco, Vishal Kumar
et al.
Non-destructive subsurface imaging methods based on the absorption or scattering of photons or neutrons are becoming increasingly popular in cultural asset conservation. However, these techniques are limited by physical and practical issues: their penetration depth may be insufficient for large items, and they usually necessitate transferring the objects of interest to specialised laboratories. The latter issue is recently being addressed by the development of portable sources, but artificial radiation can be harmful and is thus subjected to strict regulation. Muons are elementary particles that are abundantly and freely created in the atmosphere by cosmic-ray interactions. Their absorption and scattering in matter are respectively dependent on the density and elemental composition of the substance they traverse, suggesting that they could be used for subsurface remote imaging. This novel technique, dubbed "muography", has been used in applications ranging from geophysics to archaeology, but has remained largely unexplored for a wide range of cultural heritage objects that are small by muography standards but whose size and density are too large for conventional imaging methods. This document outlines the general arguments and some early simulation studies that aim at exploring the low-size limit of muography and its relevance for cultural heritage preservation.
Stars are fossils that retain the history of their host galaxies. Elements heavier than helium are created inside stars and are ejected when they die. From the spatial distribution of elements in galaxies, it is therefore possible to constrain the physical processes during galaxy formation and evolution. This approach, Galactic archaeology, has been popularly used for our Milky Way Galaxy with a vast amount of data from Gaia satellite and multi-object spectrographs to understand the origins of sub-structures of the Milky Way. Thanks to integral field units, this approach can also be applied to external galaxies from nearby to distant universe with the James Webb Space Telescope. In order to interpret these observational data, it is necessary to compare with theoretical predictions, namely chemodynamical simulations of galaxies, which include detailed chemical enrichment into hydrodynamical simulations from cosmological initial conditions. These simulations can predict the evolution of internal structures (e.g., metallicity radial gradients) as well as that of scaling relations (e.g., the mass-metallicity relations). After explaining the formula and assumptions, we will show some example results, and discuss future prospects.
Academic citation and social attention measure different dimensions of the impact of research results. Both measures do not correlate with each other, and they are influenced by many factors. Among these factors are the field of research, the type of access, and co-authorship. In this study, the increase in the impact due to co-authorship in scientific articles disaggregated by field of research and access type, was quantified. For this, the citations and social attention accumulated until the year 2021 by a total of 244,880 research articles published in the year 2018, were analyzed. The data source was Dimensions.ai, and the units of study were research articles in Economics, History and Archaeology, and Mathematics. As the main results, a small proportion of the articles received a large part of the citations and most of the social attention. Both citations and social attention in-creased, in general, with the number of co-authors. Thus, the greater the number of co-authors, the greater the probability of being cited in academic articles and mentioned on social media. The advantage in citation and social attention due to collaboration is independent of the access type for the publication. Furthermore, although collaboration with an additional co-author is in general positive in terms of citation and social attention, these positive effects reduce as the number of co-authors increases.
Seth D. Axen, Alexandra Gessner, Christian Sommer
et al.
Paleoclimatology -- the study of past climate -- is relevant beyond climate science itself, such as in archaeology and anthropology for understanding past human dispersal. Information about the Earth's paleoclimate comes from simulations of physical and biogeochemical processes and from proxy records found in naturally occurring archives. Climate-field reconstructions (CFRs) combine these data into a statistical spatial or spatiotemporal model. To date, there exists no consensus spatiotemporal paleoclimate model that is continuous in space and time, produces predictions with uncertainty, and can include data from various sources. A Gaussian process (GP) model would have these desired properties; however, GPs scale unfavorably with data of the magnitude typical for building CFRs. We propose to build on recent advances in sparse spatiotemporal GPs that reduce the computational burden by combining variational methods based on inducing variables with the state-space formulation of GPs. We successfully employ such a doubly sparse GP to construct a probabilistic model of European paleoclimate from the Last Glacial Maximum (LGM) to the mid-Holocene (MH) that synthesizes paleoclimate simulations and fossilized pollen proxy data.
We present the Deep Integral Field Spectrograph View of Nuclei of Galaxies (DIVING$^{3D}$) survey, a seeing-limited optical 3D spectroscopy study of the central regions of all 170 galaxies in the Southern hemisphere with B < 12.0 and |b| > 15 degrees. Most of the observations were taken with the Integral Field Unit of the Gemini Multi-Object Spectrograph, at the Gemini South telescope, but some are also being taken with the Southern Astrophysical Research Telescope (SOAR) Integral Field Spectrograph. The DIVING$^{3D}$ survey was designed for the study of nuclear emission-line properties, circumnuclear (within scales of hundreds of pc) emission-line properties, stellar and gas kinematics and stellar archaeology. The data have a combination of high spatial and spectral resolution not matched by previous surveys and will result in significant contributions for studies related to, for example, the statistics of low-luminosity active galactic nuclei, the ionization mechanisms in Low-Ionization Nuclear Emission-Line Regions, the nature of transition objects, among other topics.