We report on the operation of a 13 g PbWO$_4$ crystal, grown from archaeological Pb and operated as a cryogenic calorimeter in an underground environment. Read out with a Ge thermistor, the detector achieves a low energy threshold and, for the first time, enables the derivation of a dark matter exclusion limit using PbWO$_4$ as target material, for both spin-dependent interactions on neutrons and spin-independent interactions. Although limited in mass and not representative of the final RES-NOVA detector design, this prototype demonstrates effective control of mechanical vibrations and low-energy noise in a cryogenic system, which is a key requirement for rare-event searches. The experiment therefore provides a proof of principle for the RES-NOVA detection concept, validating the use of archaeological Pb-based PbWO$_4$ crystals, low-background operation, and robust data-analysis procedures. These results establish a solid technological and methodological foundation for future RES-NOVA detectors employing larger target masses and advanced thermal readout technologies.
Ruhul Amin Khalil, Asiya Jehangir, Hanane Lamaazi
et al.
The Internet of Underwater Things (IoUT) is revolutionizing marine sensing and environmental monitoring, as well as subaquatic exploration, which are enabled by interconnected and intelligent subsystems. Nevertheless, underwater communication is constrained by narrow bandwidth, high latency, and strict energy constraints, which are the source of efficiency problems in traditional data-centric networks. To tackle these problematic issues, this work provides a survey of recent advances in Semantic Communication (SC) for IoUT, a novel communication paradigm that seeks to harness not raw symbol information but rather its meaning and/or contextual significance. In this paper, we investigate the emerging advanced AI-powered frameworks, including large language models (LLMs), diffusion-based generative encoders, and federated learning (FL), that bridge semantic compression with context-aware prioritization and robust information reconstruction over noisy underwater channels. Hybrid acoustic-optical-RF architectures and edge-intelligent semantic encoders are also considered enablers of sustainable, adaptive operations. Examples in underwater archaeology, marine ecology, and autonomous underwater vehicles (AUVs) coordination are provided as a relief to illustrate the merits of meaning-driven connectivity. The paper concludes with some recommendations, including semantic representations standardization, cross-domain interpolation, and privacy-support schemes. These issues must be addressed in the future before trustworthy SC-enabled IoUT systems can be developed for underwater communication.
Kancharagunta Kishan Babu, Ashreen Tabassum, Bommakanti Navaneeth
et al.
In recent years, there has been a surge of research focused on underwater image enhancement using Generative Adversarial Networks (GANs), driven by the need to overcome the challenges posed by underwater environments. Issues such as light attenuation, scattering, and color distortion severely degrade the quality of underwater images, limiting their use in critical applications. Generative Adversarial Networks (GANs) have emerged as a powerful tool for enhancing underwater photos due to their ability to learn complex transformations and generate realistic outputs. These advancements have been applied to real-world applications, including marine biology and ecosystem monitoring, coral reef health assessment, underwater archaeology, and autonomous underwater vehicle (AUV) navigation. This paper explores all major approaches to underwater image enhancement, from physical and physics-free models to Convolutional Neural Network (CNN)-based models and state-of-the-art GAN-based methods. It provides a comprehensive analysis of these methods, evaluation metrics, datasets, and loss functions, offering a holistic view of the field. Furthermore, the paper delves into the limitations and challenges faced by current methods, such as generalization issues, high computational demands, and dataset biases, while suggesting potential directions for future research.
Pairwise compatibility calculation is at the core of most fragments-reconstruction algorithms, in particular those designed to solve different types of the jigsaw puzzle problem. However, most existing approaches fail, or aren't designed to deal with fragments of realistic geometric properties one encounters in real-life puzzles. And in all other cases, compatibility methods rely strongly on the restricted shapes of the fragments. In this paper, we propose an efficient hybrid (geometric and pictorial) approach for computing the optimal alignment for pairs of fragments, without any assumptions about their shapes, dimensions, or pictorial content. We introduce a new image fragments dataset generated via a novel method for image fragmentation and a formal erosion model that mimics real-world archaeological erosion, along with evaluation metrics for the compatibility task. We then embed our proposed compatibility into an archaeological puzzle-solving framework and demonstrate state-of-the-art neighborhood-level precision and recall on the RePAIR 2D dataset, directly reflecting compatibility performance improvements.
Ryota Hatami, Nozomu Tominaga, Takashi Yoshida
et al.
We present the results of simulations of nucleosynthesis in a core-collapse supernova (CCSN) including the neutrino process. Using the Si layer of $13M_\odot$ zero-metal progenitor as the initial composition, we calculate the nucleosynthesis by adopting the temperature, density, neutrino flux, and duration of nucleosynthesis as arbitrary parameters and compare the results with the observed abundances ratio of Sc, Ti, and V in very metal-poor (VMP) stars taken from the Stellar Abundances for Galactic Archaeology (SAGA) database. As a result, for the first time, we identify the quantitative requirements on local physical conditions. To reproduce the abundances ratios in the VMP stars, the explosive nucleosynthesis should take place under the neutrino exposure, which is time integration of neutrino flux, of $σ_ν\sim 10^{35}\,\mathrm{erg~cm^{-2}}$ and temperature of $2.0\,\mathrm{GK}\leq T \leq 3.2\,\mathrm{GK}$. The dependence on the density and each value of the neutrino flux and the duration of nucleosynthesis is weak. We also discuss whether the quantitative requirements are realized during the explosion. Although the requirements are difficult to be realized in the one-dimensional simulations, the non-monotonic thermal evolution shown in recent three-dimensional simulations may satisfy them. Because the evolution is likely caused by turbulent motion stemming from the initial asphericity of the progenitor, it is important to calculate the long-term three-dimensional supernova explosion of multi-dimensional metal-free progenitor models and follow the nucleosynthesis self-consistently.
Morgane Bos, Damien Claeys, Dorothée Stiernon
et al.
The concept of <i>value</i> seems to have capital importance both in the scientific literature and in various heritage actors’ professional discourse. The actions undertaken to develop the built environment inherited from previous generations seem to depend on the value we assign it. In this essay, the concepts of value, assessor, and heritage assessment are discussed. Two historical contexts are compared: the classical axiology of the 19th century based on the Enlightenment search for rationality and the typologies of contemporary values struggling with complexity. This historical reassessment shows a complexification and multiplication of evaluation grids, as well as the need to question the subjectivity inherent in heritage actors’ decisions. In order to not sink into excessive relativism definitively discrediting any attempt to make the process of heritage assessments more objective, a dynamic point of view is proposed, linking the constructivist approach with the use of a criteria-based value grid.
Riley C. W. O'Neill, Katrina Yezzi-Woodley, Jeff Calder
et al.
Modern archaeological methods increasingly utilize 3D virtual representations of objects, computationally intensive analyses, high resolution scanning, large datasets, and machine learning. With higher resolution scans, challenges surrounding computational power, memory, and file storage quickly arise. Processing and analyzing high resolution scans often requires memory-intensive workflows, which are infeasible for most computers and increasingly necessitate the use of super-computers or innovative methods for processing on standard computers. Here we introduce a novel protocol for en-masse micro-CT scanning of small objects with a {\em mostly-automated} processing workflow that functions in memory-limited settings. We scanned 1,112 animal bone fragments using just 10 micro-CT scans, which were post-processed into individual PLY files. Notably, our methods can be applied to any object (with discernible density from the packaging material) making this method applicable to a variety of inquiries and fields including paleontology, geology, electrical engineering, and materials science. Further, our methods may immediately be adopted by scanning institutes to pool customer orders together and offer more affordable scanning. The work presented herein is part of a larger program facilitated by the international and multi-disciplinary research consortium known as Anthropological and Mathematical Analysis of Archaeological and Zooarchaeological Evidence (AMAAZE). AMAAZE unites experts in anthropology, mathematics, and computer science to develop new methods for mass-scale virtual archaeological research. Overall, our new scanning method and processing workflows lay the groundwork and set the standard for future mass-scale, high resolution scanning studies.
Andrea Giammanco, Marwa Al Moussawi, Matthieu Boone
et al.
In cultural heritage conservation, it is increasingly common to rely on non-destructive imaging methods based on the absorption or scattering of photons ($X$ or $γ$ rays) or neutrons. However, physical and practical issues limit these techniques: their penetration depth may be insufficient for large and dense objects, they require transporting the objects of interest to dedicated laboratories, artificial radiation is hazardous and may induce activation in the material under study. Muons are elementary particles abundantly and freely produced in cosmic-ray interactions in the atmosphere. Their absorption and scattering in matter are characteristically dependent on the density and elemental composition of the material that they traverse, which offers the possibility of exploiting them for sub-surface remote imaging. This novel technique, nicknamed "muography", has been applied in use cases ranging from geophysics to archaeology to nuclear safety, but it has been so far under-explored for a vast category of cultural heritage objects that are relatively large (from decimeters to human size) and dense (stone, metals). The development of portable muon detectors makes muography particularly competitive in cases where the items to be analysed are not transportable, or set up in a confined environment. This document reviews the relevant literature, presents some exemplary use cases, and critically assesses the strengths and weaknesses of muography in this context.
Wei-Lun Huang, Davood Tashayyod, Amir Gandjbakhche
et al.
The depth of field of a camera is a limiting factor for applications that require taking images at a short subject-to-camera distance or using a large focal length, such as total body photography, archaeology, and other close-range photogrammetry applications. Furthermore, in multi-view capture, where the target is larger than the camera's field of view, an efficient way to optimize surface coverage captured with quality remains a challenge. Given the 3D mesh of the target object and camera poses, we propose a novel method to derive a focus distance for each camera that optimizes the quality of the covered surface area. We first design an Expectation-Minimization (EM) algorithm to assign points on the mesh uniquely to cameras and then solve for a focus distance for each camera given the associated point set. We further improve the quality surface coverage by proposing a $k$-view algorithm that solves for the points assignment and focus distances by considering multiple views simultaneously. We demonstrate the effectiveness of the proposed method under various simulations for total body photography. The EM and $k$-view algorithms improve the relative cost of the baseline single-view methods by at least $24$% and $28$% respectively, corresponding to increasing the in-focus surface area by roughly $1550$ cm$^2$ and $1780$ cm$^2$. We believe the algorithms can be useful in a number of vision applications that require photogrammetric details but are limited by the depth of field.
Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.
The study of ancient writings has great value for archaeology and philology. Essential forms of material are photographic characters, but manual photographic character recognition is extremely time-consuming and expertise-dependent. Automatic classification is therefore greatly desired. However, the current performance is limited due to the lack of annotated data. Data generation is an inexpensive but useful solution for data scarcity. Nevertheless, the diverse glyph shapes and complex background textures of photographic ancient characters make the generation task difficult, leading to the unsatisfactory results of existing methods. In this paper, we propose an unsupervised generative adversarial network called AGTGAN. By the explicit global and local glyph shape style modeling followed by the stroke-aware texture transfer, as well as an associate adversarial learning mechanism, our method can generate characters with diverse glyphs and realistic textures. We evaluate our approach on the photographic ancient character datasets, e.g., OBC306 and CSDD. Our method outperforms the state-of-the-art approaches in various metrics and performs much better in terms of the diversity and authenticity of generated samples. With our generated images, experiments on the largest photographic oracle bone character dataset show that our method can achieve a significant increase in classification accuracy, up to 16.34%.
Stefan Kraus, Daniel Mortimer, Sorabh Chhabra
et al.
We present science cases and instrument design considerations for the BIFROST instrument that will open the short-wavelength (Y/J/H-band), high spectral dispersion (up to R=25,000) window for the VLT Interferometer. BIFROST will be part of the Asgard Suite of instruments and unlock powerful venues for studying accretion & mass-loss processes at the early/late stages of stellar evolution, for detecting accreting protoplanets around young stars, and for probing the spin-orbit alignment in directly-imaged planetary systems and multiple star systems. Our survey on GAIA binaries aims to provide masses and precision ages for a thousand stars, providing a legacy data set for improving stellar evolutionary models as well as for Galactic Archaeology. BIFROST will enable off-axis spectroscopy of exoplanets in the 0.025-1" separation range, enabling high-SNR, high spectral resolution follow-up of exoplanets detected with ELT and JWST. We give an update on the status of the project, outline our key technology choices, and discuss synergies with other instruments in the proposed Asgard Suite of instruments.
Panagiotis Agrafiotis, Konstantinos Karantzalos, Andreas Georgopoulos
Mapping the seafloor with underwater imaging cameras is of significant importance for various applications including marine engineering, geology, geomorphology, archaeology and biology. For shallow waters, among the underwater imaging challenges, caustics i.e., the complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface, is likely the most crucial one. Caustics is the main factor during underwater imaging campaigns that massively degrade image quality and affect severely any 2D mosaicking or 3D reconstruction of the seabed. In this work, we propose a novel method for correcting the radiometric effects of caustics on shallow underwater imagery. Contrary to the state-of-the-art, the developed method can handle seabed and riverbed of any anaglyph, correcting the images using real pixel information, thus, improving image matching and 3D reconstruction processes. In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics". Then, exploits the 3D geometry of the scene to achieve a pixel-wise correction, by transferring appropriate color values between the overlapping underwater images. Moreover, to fill the current gap, we have collected, annotated and structured a real-world caustic dataset, namely R-CAUSTIC, which is openly available. Overall, based on the experimental results and validation the developed methodology is quite promising in both detecting caustics and reconstructing their intensity.
A ciência é permeada por dificuldades e crises, seja nas pesquisas de campo ou nas reflexões teórico-metodológicas, que, ao invés de indicarem impossibilidades, alimentam as teorias científicas. Enquanto disciplina, a antropologia convive há tanto tempo com crises, em especial de autoridade, objeto e representação, que alguns autores consideram que exatamente por estar em uma perpétua crise, esta constitui-se como crítica e indisciplinada. Com a ideia de que crises e dificuldades podem gerar impulsionamentos metodológicos e reflexivos, na primeira parte do artigo discutem-se estratégias de escrita de três etnografias clássicas e três mais recentes; na segunda, analiso um seminário organizado e ministrado por travestis e transexuais, no qual demarcaram-se tensionamentos entre estas e pesquisadoras e pesquisadores cis. Ao final, considera-se que mesmo não havendo receitas prontas de como manejar crises metodológicas, produções antropológicas e transativistas podem contribuir para que tais crises gerem impulsionamentos reflexivos.
The Veterani Cave is one of the most well-known natural caves on the territory of Romania, nowadays being often visited by tourists. It is not only an impressive natural monument but also a multilayered archaeological site with a very rich history. The fortification of the grotto is certain for the Middle Ages and the modern era. The traces of the Roman era are less clear, with specialists hesitating between recognizing it as a fishing settlement that at some point, in the late Roman era, could have been fortified, or classifying it as a sacred cave or even a mithraeum. As it is expected, its strategic position in the area of the Danube Cauldrons strait made this unusual place to be included in the UNESCO list of the Danube Limes of the Roman Empire. In the following text, we try to decipher the character of the Roman ruins at Veterani Cave (Peskabara) corroborating all the sources at our disposal: archaeological, epigraphical, archival and cartographical information. We consider that in front of the cave there existed, starting from the end of the 3rd century AD, a small fort (similar to a quadriburgium) with a small port, also defended by walls made from stone and bricks.
Through the use of examples, we explain one way in which applied topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global shape of a dataset, such as the three-circle model for $3 \times 3$ pixel patches from natural images, or the configuration space of the cyclo-octane molecule, which is a sphere with a Klein bottle attached via two circles of singularity. In these studies of global shape, short persistent homology bars are disregarded as sampling noise. More recently, however, persistent homology has been used to address questions about the local geometry of data. For instance, how can local geometry be vectorized for use in machine learning problems? Persistent homology and its vectorization methods, including persistence landscapes and persistence images, provide popular techniques for incorporating both local geometry and global topology into machine learning. Our meta-hypothesis is that the short bars are as important as the long bars for many machine learning tasks. In defense of this claim, we survey applications of persistent homology to shape recognition, agent-based modeling, materials science, archaeology, and biology. Additionally, we survey work connecting persistent homology to geometric features of spaces, including curvature and fractal dimension, and various methods that have been used to incorporate persistent homology into machine learning.
Reconstructing the structure of the soil using non-invasive techniques is a very relevant problem in many scientific fields, like geophysics and archaeology. This can be done, for instance, with the aid of Frequency Domain Electromagnetic (FDEM) induction devices. Inverting FDEM data is a very challenging inverse problem, as the problem is extremely ill-posed, i.e., sensible to the presence of noise in the measured data, and non-linear. Regularization methods substitute the original ill-posed problem with a well-posed one whose solution is an accurate approximation of the desired one. In this paper we develop a regularization method to invert FDEM data. We propose to determine the electrical conductivity of the ground by solving a variational problem. The minimized functional is made up by the sum of two term: the data fitting term ensures that the recovered solution fits the measured data, while the regularization term enforces sparsity on the Laplacian of the solution. The trade-off between the two terms is determined by the regularization parameter. This is achieved by minimizing an $\ell_2 - \ell_q$ functional with $0 < q \leq 2$. Since the functional we wish to minimize is non-convex, we show that the variational problem admits a solution. Moreover, we prove that, if the regularization parameter is tuned accordingly to the amount of noise present in the data, this model induces a regularization method. Some selected numerical examples on synthetic and real data show the good performances of our proposal.