Considerable observational evidence suggests that the activity of supermassive black holes in galactic nuclei is transient. The term ``active galactic nuclei archaeology'' has even been coined. This implies the possibility of reconstructing the history of activity, such as changes in the nuclear luminosity over time across various regions of the electromagnetic spectrum, by analysing how this activity manifested itself on galactic and extragalactic spatial scales. These phenomena include relic radio structures, gas clouds illuminated by the ``ionising echo'' of past activity, and Fermi/eROSITA bubbles. We provide a review of the results of galactic nucleus activity studies, focusing on its observable impact on the intergalactic medium and circumgalactic environment. Our main focus is on optical observations of ionisation cones and evidence of switching between radiative (ionisation cones) and kinetic (radio jets) modes of nuclear activity.
Galactic archaeology--the study of stellar migration histories--provides insights into galaxy formation and evolution. However, establishing causal relationships between observable stellar properties and their birth conditions remains challenging, as key properties like birth radius are not directly observable. We employ Rank-based Latent Causal Discovery (RLCD) to uncover the causal structure governing the chemodynamics of a simulated Milky Way galaxy. Using only five observable properties (metallicity, age, and orbital parameters), we recover in a purely data-driven manner a causal graph containing two latent nodes that correspond to real physical properties: the birth radius and guiding radius of stars. Our study demonstrates the potential of causal discovery models in astrophysics.
Nivedita Sinha, Bharati Khanijo, Sanskar Singh
et al.
In this paper, we describe a multi-modal search system designed to search old archaeological books and reports. This corpus is digitally available as scanned PDFs, but varies widely in the quality of scans. Our pipeline, designed for multi-modal archaeological documents, extracts and indexes text, images (classified into maps, photos, layouts, and others), and tables. We evaluated different retrieval strategies, including keyword-based search, embedding-based models, and a hybrid approach that selects optimal results from both modalities. We report and analyze our preliminary results and discuss future work in this exciting vertical.
يناقش هذا البحث تلابوقا خان سادس حكام دولة مغول القبجاق تلك البلاد التي تعرف ايضاً باسم دولة مغول القبيلة الذهبية والغوص بسيرته الشخصية وحروبه الخارجية كالحرب التي دار رحاها مع الدولة الايلخانية تلك الدولة المغولية الاخرى التي اسسها هولاكو خان وقامت على انقاض الخلافة العباسية مروراً بحرب تلابوقا خان في بلاد الكرل (هنغاريا) .
كما تضمن البحث تأثير مقتل تلابوقا خان على اوضاع مسلمي القبجاق على يد خصمه طقطاي الذي كان يعتنق الديانة الشامانية المغولية إذ قطع مقتل هذا الخان سلسلة من الحكام المسلمين الذين تعاقبوا على زعامة بلاد القبجاق ,ام ان تاثير الدين يبقى محدودا في السياسة المغولية وفق الاسس والمبادئ التي وضعها جنكيزخان مؤسس الامبراطورية المغولية التي تقضي بالولاء السياسي المطلق للمغول ويترك مسالة اختيار الدين حرية شخصية من حكام ورعايا طالما التزموا بالقوانين المغولية
Raveerat Jaturapitpornchai, Giulio Poggi, Gregory Sech
et al.
Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images. This paper investigates the impact of visualisations on deep learning performance through a comprehensive testing framework. The study involves the use of eight semantic segmentation models to evaluate seven diverse visualisations across two study areas, encompassing five archaeological classes. Experimental results reveal that the choice of appropriate visualisations can influence performance by up to 8%. Yet, pinpointing one visualisation that outperforms the others in segmenting all archaeological classes proves challenging. The observed performance variation, reaching up to 25% across different model configurations, underscores the importance of thoughtfully selecting model configurations and LiDAR visualisations for successfully segmenting archaeological objects.
La sostanza onirica è una componente importante della narrazione e della sostanza epifanica che caratterizza la Vita nova. Collocati in punti strategici della storia, tutti convergenti verso il (o dal) kérigma della morte o meglio assunzione al cielo di Beatrice, questi episodi si configurano come premonizioni e come aperture dell’orizzonte narrativo. In questo contributo sono analizzate le due, forse tre visiones in somniis, che si trovano rispettivamente nei paragrafi III, XII e XLII. Nella comune dimensione onirica e nella prefigurazione di qualcosa che avverrà tutte e tre mantengono un carattere enigmatico.
Mirela Džehverović, Amela Pilav, Belma Jusić
et al.
Numerous archaeological sites in Bosnia and Herzegovina represent a historical heritage and testify to the rich cultural, social, and political life of medieval Bosnia. Bobovac, the capital of the Bosnian Kingdom after King Tvrtko I's coronation in 1377, featured a royal complex with a palace, church, and fortification. Recent molecular-genetic research on skeletal remains from Bobovac aims to uncover medieval ancestors' customs and genetic origins. Fifteen well-preserved teeth samples from Bobovac were processed. STR amplification employed PowerPlex® Fusion and Investigator® 24plex QS Kits, with Y-STR profiles generated using the PowerPlex® Y23 System. Fourteen partial autosomal STR profiles were obtained, enabling sex determination and kinship analysis. STR amplification success varied due to ancient DNA degradation, with larger loci showing lower amplification rates. Kinship analysis confirmed appropriate marker selection, demonstrating high reliability for determining close relationships. Integrating aDNA analysis with archaeological research enhances our understanding of historical populations, connecting archaeology and forensic genetics to contribute to the broader narrative of human history.
Théophane Nicolas, Ronan Gaugne, Bruno Arnaldi
et al.
The IRMA project aims to design innovative methodologies for research in the field of historical and archaeological heritage based on a combination of medical imaging technologies and interactive 3D restitution modalities (virtual reality, augmented reality, haptics, additive manufacturing). These tools are based on recent research results from a collaboration between IRISA, Inrap and the company Image ET and are intended for cultural heritage professionals such as museums, curators, restorers and archaeologists.
Gregory Sech, Paolo Soleni, Wouter B. Verschoof-van der Vaart
et al.
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawback. However, there is still a need to explore its effectiveness when applied across different archaeological datasets. This paper compares the performance of various transfer learning configurations using two semantic segmentation deep neural networks on two LiDAR datasets. The experimental results indicate that transfer learning-based approaches in archaeology can lead to performance improvements, although a systematic enhancement has not yet been observed. We provide specific insights about the validity of such techniques that can serve as a baseline for future works.
Kent K. Chang, Mackenzie Cramer, Sandeep Soni
et al.
In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks. We argue that this supports a case for open models whose training data is known.
The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding.
We develop an AI application for archaeological dating of bronze Dings. A classification model is employed to predict the period of the input Ding, and a detection model is used to show the feature parts for making a decision of archaeological dating. To train the two deep learning models, we collected a large number of Ding images from published materials, and annotated the period and the feature parts on each image by archaeological experts. Furthermore, we design a user system and deploy our pre-trained models based on the platform of WeChat Mini Program for ease of use. Only need a smartphone installed WeChat APP, users can easily know the result of intelligent archaeological dating, the feature parts, and other reference artifacts, by taking a photo of a bronze Ding. To use our application, please scan this QR code by WeChat.