Classes Of Artifacts Research Articles

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Generative artificial intelligence (genAI) language models have become firmly embedded in public consciousness. Their abilities to extract and summarise information from a wide range of sources in their training data have attracted the attention of many scholars. This paper examines how four genAI large language models (ChatGPT, GPT4, DeepAI, and Google Bard) responded to prompts, asking (i) whether artificial intelligence would affect how cultural heritage will be managed in the future (with examples requested) and (ii) what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions. The genAI systems provided a range of examples, commonly drawing on and extending the status quo. Without a doubt, AI tools will revolutionise the execution of repetitive and mundane tasks, such as the classification of some classes of artifacts, or allow for the predictive modelling of the decay of objects. Important examples were used to assess the purported power of genAI tools to extract, aggregate, and synthesize large volumes of data from multiple sources, as well as their ability to recognise patterns and connections that people may miss. An inherent risk in the ‘results’ presented by genAI systems is that the presented connections are ‘artifacts’ of the system rather than being genuine. Since present genAI tools are unable to purposively generate creative or innovative thoughts, it is left to the reader to determine whether any text that is provided by genAI that is out of the ordinary is meaningful or nonsensical. Additional risks identified by the genAI systems were that some cultural heritage professionals might use AI systems without the required level of AI literacy and that overreliance on genAI systems might lead to a deskilling of general heritage practitioners.

Towards an archaeology of everyday life in British Ionian Islands: the cultural itineraries of the Kythera Gin Bottles

SUMMARY During the course of the nineteenth century the island of Kythera, Southwest Greece, was part of the United States of Ionian Islands, under the control of the British empire. This paper explores the materiality of colonialism in local contexts, through the object itineraries of a group of stoneware bottles—a class of artefacts that continues to be represented in domestic assemblages locally today. We examine the diverse ways the vessels became entangled with the lives of individuals, colonialist agendas, and collective perceptions of identity, seeking to identify how the meanings associated with the objects and their relational connections shifted across time and space.

Objectionable Commemorations: Ethical and Political Issues

AbstractThe term, "objectionable commemorations”, refers to a broad category of public artefacts – such as, and especially, memorials, monuments and statues – that are regarded as morally problematic in virtue of what or whom they honour. In this regard, they are a special class of public artefacts that are subject to public contestation. In this paper, we survey the general ethical and political issues on this topic. First, we categorise the arguments on offer in the literature, concerning the objectionable nature of such commemorations. Second, we review common political responses to objectionable commemorations. Finally, we identify fruitful areas for further philosophical inquiry on this topic.

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training

Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods. In particular, the scarcity of artefact-containing scans available for training has been a major obstacle in the development and implementation of machine learning in clinical research. To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts. Our novel contributions are threefold: first, we use the novel physics-based artefact generators to generate synthetic brain MRI scans with controlled artefacts as a data augmentation technique. This will avoid the labour-intensive collection and labelling process of scans with rare artefacts. Second, we propose a large pool of abstract and engineered image features developed to identify 9 different artefacts for structural MRI. Finally, we use an artefact-based feature selection block that, for each class of artefacts, finds the set of features that provide the best classification performance. We performed validation experiments on a large data set of scans with artificially-generated artefacts, and in a multiple sclerosis clinical trial where real artefacts were identified by experts, showing that the proposed pipeline outperforms traditional methods. In particular, our data augmentation increases performance by up to 12.5 percentage points on the accuracy, F1, F2, precision and recall. At the same time, the computation cost of our pipeline remains low –less than a second to process a single scan– with the potential for real-time deployment. Our artefact simulators obtained using adversarial learning enable the training of a quality control system for brain MRI that otherwise would have required a much larger number of scans in both supervised and unsupervised settings. We believe that systems for quality control will enable a wide range of high-throughput clinical applications based on the use of automatic image-processing pipelines.

On the Status of the Boar Tooth Artifact from the Backdirt of Vogelherd

During the course of the excavations in the backdirt of Vogelherd in 2008, the crew recovered a tusk of a boar preserving clear signs of anthropogenic modification. In addition to the polishing that regularly occurs on tusks during the life of the animal, this specimen, showed signs of cut- ting, scraping and polish subsequent to the animal’s death. This artifact superficially resembles a Magdalenian female figurine with a triangular buttock and a stick-like torso, which led to the hypothesis that the find may be of Paleolithic origin and may be an artwork. Alternatively, the find was hypothesized to be an artifact of unknown function from the Holocene, based on the rarity of faunal remains of boar from the Paleolithic of the Swabian Jura. While the excavator always stressed the uncertainty related to the interpretation of the find, some colleagues ac- cepted the hypothesis that the find represented a Magdalenian female figurine. Thirteen years after its recovery, we have reexamined this artifact and conducted comparative studies that support the hypothesis that the find is of Holocene age. Based on similar finds, we conclude that, although the function of this class of artifact is not yet certain, this artifact is a Neolithic or Mesolithic tool with clear parallels in Germany, France and Switzerland. This class of artifact is rare in Central Europe, but our survey of Neolithic and Mesolithic publications allows us to reject the hypothesis, that this find represents a Magdalenian female figurine. This artifact, however, enriches the record of Holocene organic technology in southwestern Germany.

Reassessing the terminal ballistic performance of trilobate and quadrilobate arrow points on Iron Age battlefields.

In the Eurasian Iron Age arrow points comprise a prominent class of artifact. Projectile experiments are useful for studying the ballistic performance of ancient arrow points and implications of arrow point innovations in warfare and shifting socio politics in Eurasia. However, when projectile experiments are not representative of past weapon use, they can lead to misinterpretations of the archaeological record. Notable problems arise when homogeneous target simulants used in controlled experiments are not representative of the targets past weapons were designed to encounter. This article explores the relationship between arrow point morphology and design choices in the Iron Age using different target media. Shooting arrow points into pottery clay leads to the conclusion that more blades reduced penetrating performance on ancient battlefields, but a very different result obtains by shooting the same points into thick tooling leather as a simulant for leather body armor. The results help explain patterns observed in the Eurasian archaeological record, where trilobate arrow points-initially developed by lightly armored horse archers on the Eurasian steppe-were increasingly adopted by a wide range of societies across Eurasia throughout the Iron Age.

Research on the Composition Analysis and Identification of Ancient Glass Products Based on Decision Tree and K-means Clustering Algorithm

Ancient glass is susceptible to weathering by environmental influences, resulting in changes in its chemical composition and proportions, which affects the correct judgment of its category by archaeologists. In order to correctly analyze and identify the composition of ancient glass artifacts, firstly, this paper establishes a decision tree model to derive the basis for determining the type of glass artifacts. Secondly, the aggregation coefficients of high potassium glass and lead-barium glass were plotted separately in this paper, and the K-value of clustering was determined to be 2 according to the elbow rule. After using principal component analysis to reduce the dimensionality of the chemical composition content data, the K-means clustering algorithm was used to classify the subclasses of high potassium glass and lead-barium glass respectively. Finally, the established model was used to identify the chemical composition and its type of unknown class of glass artifacts.

Exploration And Analysis of Weathering Factors of Glass Artefacts Based on Cluster Analysis and Grey Correlation Analysis

The aim of this paper is to investigate the relationship between the chemical composition of ancient glass artefacts after weathering and to construct a model for classifying and predicting the class of artefacts based on some of the characteristics of the ancient glass artefacts obtained. Cardinality tests, information entropy, cluster analysis, support vector machines and grey correlation are mainly used to conduct the study. Through chi-square test, the relationship between weathering of glass relics and glass type, glass color and glass pattern is explained. Finally, the relationship between weathering and glass type is closer. Information entropy was used to explain the relationship between the weathering of cultural relics and the content of chemical components contained in cultural relics. Among them, potassium oxide ( O) and calcium oxide (CaO) had the largest information gain. The effective information gain of lead barium is silicon dioxide (Si ), potassium oxide (PbO), copper oxide (CuO) and sodium oxide ( O). By using cluster analysis, the law analysis and corresponding subcategories of high-potassium cultural relics and lead-barium cultural relics are made, and 3-5 subcategories are formed. Support vector machine was used to judge the type of glass relics, and the final result was that A1, A6 and A7 were high potassium. A2, A3, A4, A5, A8 are lead barium; The correlation coefficient matrix was used to analyze the correlation between the chemical components in different kinds of cultural relics, and the differences were explained based on the results of grey correlation degree analysis. Finally, the conclusion was drawn: in the firing process of high-potassium glass, the substance with high potassium content as the co-solvent did not have an essential effect on the formation of high-potassium glass. However, lead barium glass is different. As lead ore (high content of lead oxide and barium oxide) is added as co-solvent in the firing process, lead barium glass produced by firing contains a large amount of lead oxide and barium oxide.

IMPACT OF OSCILLOMETRIC MEASUREMENT ARTEFACTS IN AMBULATORY BLOOD PRESSURE MONITORING ON ESTIMATES OF AVERAGE BLOOD PRESSURE AND OF ITS VARIABILITY: A PILOT STUDY

Objective: Ambulatory blood pressure monitoring (ABPM) plays an important role in the diagnosis of hypertension. However, methodological factors and the measurement conditions affect the results and may lead to incorrect classification of the patient. We performed a pilot study to evaluate the impact of oscillometric measurement artefacts on ABPM-derived variables. Methods: Four classes of artefacts have been detected: motor activity artefacts, cuff errors, cardiovascular arousals, and arrhythmias. These artefacts were identified via simultaneous recording of actigraphy, cuff pressure, and an ECG. The data consisted of uncorrected measurements (all data), corrected measurements (all artefact free data), and artefact affected data. Design and method: A total of 30 individuals (9 female / 21 male, aged between 36 and 86 years, mean: 65.5 (SD: 9.5) were included in the study. The 24h average blood pressure (BP) was higher in artefacts-affected measurements compared the artefact-free measurements both for systolic (4.6 mmHg) and diastolic (1.3 mmHg) measurements. Further, artefact-affected systolic BP (SBP) was 6.4 mmHg higher than artefact-free measurements during daytime. On average, nocturnal measurements showed no artefact-depended differences. However, individual comparisons yielded that 23% of the subjects crossed the threshold for BP classification for either 24-h, daytime or nocturnal hypertension when comparing uncorrected and artefact-free measurements. Dipping classification changed in 24% of subjects. BP variability was 21% and 12% higher for SPB and DBP, respectively, during daytime when comparing uncorrected and artefact-free measurements. These differences were even higher (27% for SBP and 21% for DPB) when focussing on night-time BP variability. Conclusions: The study reveals that measurement artefacts are frequently present during cuff-based ABPM and do relevantly affect measurement outcome. Exclusion of measurement artefacts is a promising approach to improving cuff-based ABPM accuracy.

An analytical identification model for ancient glass based on support vector machine and cluster analysis

Glass artifacts are easily weathered by environmental influences during burial, and the internal chemical composition is exchanged with the external environment, resulting in changes in its composition ratio. For archaeological interrogation, it is of great significance to study the chemical composition of glass before and after weathering and related issues. In this paper, a support vector machine model is used to reasonably separate the data points of high potassium glass and lead-barium glass, and a linear classification function is derived, i.e., the classification law of the two types of glass is obtained. Then, this paper performs R-type cluster analysis on the chemical composition indexes first, and reduces the dimensionality of several indexes; then performs Q-type cluster analysis on the sample detection points, and performs a one-way ANOVA test on the classification scheme of different numbers of subclasses obtained, and finally divides them into four subclasses. On this basis, the data of the unknown class of glass artifacts are substituted into the trained support vector machine model, and the type to which they belong can be determined by calculating the value of the judgment linear classification function.

From settlement patterns to memory of place among Holocene hunter-gatherers at Sai Island, Middle Nile Valley

ABSTRACT Persistent places are the locations where people aggregate, utilise and reuse natural or built features and develop their social identities and interactions. A network of persistent places forms interconnected persistent settlement patterns, which create a humanly made or storied landscape with a shared community-based memory of place. Although it has been demonstrated that persistent settlement patterns long preceded the onset of Neolithic farming villages, the Levant has received the most attention regarding this perspective. At the same time, Africa still needs to be addressed. This paper provides an illustrative case study of persistent settlement patterns created by Holocene hunter-gatherers in the Middle Nile Valley of Sudan. It employs geostatistical patterning and visual mapping of an extensive collection of different classes of artefacts (lithic industry, hammerstones, ground stone tools and pottery) from a well-preserved Khartoum Variant site on Sai Island and correlates them to complex architectural features exposed on an archaeological surface (Level 1) at site 8-B-10C dating to the early fifth millennium cal. BC. The combination of multiple in-depth geostatistical analyses of a complex habitation system successfully documents an exceptionally preserved planned intra-site settlement organisation maintained over several generations, revealing a memory of place.

SvCapture: efficient and specific detection of very low frequency structural variant junctions by error-minimized capture sequencing.

Error-corrected sequencing of genomic targets enriched by probe-based capture has become a standard approach for detecting single-nucleotide variants (SNVs) and small insertion/deletions (indels) present at very low variant allele frequencies. Less attention has been given to comparable strategies for rare structural variant (SV) junctions, where different error mechanisms must be addressed. Working from samples with known SV properties, we demonstrate that duplex sequencing (DuplexSeq), which demands confirmation of variants on both strands of a source DNA molecule, eliminates false SV junctions arising from chimeric PCR. DuplexSeq could not address frequent intermolecular ligation artifacts that arise during Y-adapter addition prior to strand denaturation without requiring multiple source molecules. In contrast, tagmentation libraries coupled with data filtering based on strand family size greatly reduced both artifact classes and enabled efficient and specific detection of single-molecule SV junctions. The throughput of SV capture sequencing (svCapture) and base-level accuracy of DuplexSeq provided detailed views of the microhomology profile and limited occurrence of de novo SNVs near the junctions of hundreds of newly created SVs, suggesting end joining as a possible formation mechanism. The open source svCapture pipeline enables rare SV detection as a routine addition to SNVs/indels in properly prepared capture sequencing libraries.

A 2D Geometric Morphometric Assessment of Chrono-Cultural Trends in Osseous Barbed Points of the European Final Palaeolithic and Early Mesolithic

Abstract Studies on prehistoric osseous barbed points have relied heavily on typology in linking presumed types to broader techno-complexes, and for making chronological inferences. The accumulation of both new finds and of radiocarbon dates obtained directly on such artefacts, however, has revealed that (i) shape variability defies neat typological divisions, and that (ii) chronological inferences based on typology often fail. To further query these issues and to better understand the design choices and cultural evolutionary dynamics within this artefact class, we present a 2D open-outline geometric morphometric analysis of 50 directly dated Late Pleistocene and Early Holocene osseous barbed points primarily from northern and western Europe. The results indicate that (a) different components (tip, base, and barbs) of these artefacts were subject to varying design constraints and that (b) there is no clear-cut distinction between Final Palaeolithic and Mesolithic point traditions. Different techno-functional components evolved at various rates while specimens assigned to the same type and/or techno-complex are only occasionally morphologically similar. The results reflect a relatively low level of normativity for this artefact class and likely a repeated convergence on similar design elements. We propose that interpretations linked to cultural dynamics, individual craft agency, and repeated convergence on locally optimal designs may offer more satisfying avenues for thinking about the barbed points of this period.

A pXRF analysis of historic brick chemical data throughout the lake Michigan Morainic region, United States

Prior research has identified and grouped localized brick samples utilizing portable X-ray fluorescence (pXRF). This work asks whether historic brick samples across a multiple-state regional area can be reliably grouped based upon pXRF data. To answer this, pXRF readings (n = 718) were taken from sites or extant buildings (n = 36) in Milwaukee, Wisconsin; Chicago, Illinois; Hobart, Indiana; South Bend, Indiana; The University of Notre Dame, Indiana; and Zeeland, Michigan. Results indicate distinct chemical groupings that correspond with and represent different brick production sites. In some cases, it is possible to identify the use of brick of more than one chemical type within the same structure. This work is important because of the historic significance of bricks as an artifact class. A rich history of brick production has become a crucial factor in the community identity for many of these sites and the results of this project indicate that matching brick samples found in archaeological sites to their production site with pXRF is promising. The results from this work also indicate that historic bricks that are to be reused for contemporary repairs and construction projects can be accurately sourced based upon chemical data supplied by pXRF analysis.

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

SOCIAL, ECONOMIC, AND POLITICAL CHANGES: AN INTEGRATION OF CERAMIC AND LITHIC DATA FROM THE THREE RIVERS REGION

Archaeological research in northwestern Belize indicates a long history of occupation beginning in the Middle Preclassic and ending with abandonment in the Terminal Classic. The collection and analysis of ceramic and lithic data on a broad regional scale and across the entire range of settlement hierarchy allow for a comprehensive examination of social and political changes that occurred across the region. Stylistic changes in the ceramics, the continuity of lithic forms, and depositional patterns of both artifact classes provide information on the activities that occurred between the Late and Terminal Classic in the Three Rivers Region

Review of Coins and Samian Ware

It is no secret that samian ware plays a key role in dating the archaeology of the western Roman Empire; the ubiquitous brown, orange and red-slipped sherds are distinctive and abundant enough for detailed study, which has in turn yielded valuable information concerning chronologies of production and deposition from the 1st century AD onwards. Since dates inferred from samian deposits have obvious implications for the dating of other artefact classes and sites, it is clear that a critical reassessment of the foundations of samian chronology is a task of considerable importance for Roman archaeology more generally. Addressing this task is one of the main aims of this monograph, a much anticipated revised edition of Anthony King’s 1985 PhD study of the chronology of samian ware in the northwest Empire during the late 2nd to mid 3rd centuries AD.

Classification and identification of ancient glass objects based on decision tree model

Glass products were one of the important goods on the Silk Road. In this paper, a decision tree model is adopted to determine the law of classification of high potassium and lead-barium glass and to obtain the criteria for classification thus illustrating the law of classification. In order to subclassify the two categories of high potassium and lead-barium, k-means clustering is used for unsupervised classification. Once a classification model was established, the unknown classes of glass artifacts were then classified using this model.

Solving the problem of composition and identification of ancient glass products based on classification model

Glass has been used as ornaments or containers in ancient times, and in recent years, with the development of the world, the existence of glass has been paid more and more attention. Examining the composition and identification of ancient glass artifacts reflects the rise and fall of glass production in each dynasty, thus reflecting the face of each dynasty, reflecting the historical understanding and importance of glass materials, which is quite helpful for archaeological research. The problem of predicting unknown classes of glass artifacts and performing sensitivity analysis is solved by a classification model based on random forest. In this paper, we first define lead-barium glass as a positive class, and build classification models for weathered and unweathered glass based on the random forest algorithm and the traditional classification algorithm, and then train them separately. ROC curve Verify the prediction effect of random forest.

A Fully Automated Digital Workflow for Assessment of Bone Marrow Cytomorphology Based on Single Cell Detection and Classification with AI

Background: Cytomorphology is an essential method of phenotype diagnostics in lab hematology. Recently, we have integrated automated scanning of peripheral blood smears and AI-based classification of blood cell images into our diagnostic routine (data reported at ASH 2021) which has been further evaluated in the BELUGA study (NCT04466059). Now we present a modified workflow for scanning and assessment of bone marrow (BM) smears including classification of all kinds of benign and malignant BM cell types. Aim: To provide a cloud-based AI tool to support manual review of BM smears. Methods: We established a 3-step fully automated and AI-based workflow starting with a 10x pre-scan for identification of areas of interest (AOI) and capture of variable numbers of AOI at 40x magnification (with oil). Resulting fields of view (FOV, 2048x1496 pixels) were processed by 2 independent deep neural networks (DNN) for object detection and classification. First, 37 BM scans (10x magnification) were annotated by hem experts using freely drawn polygon labels, distinguishing between 6 quality classes of areas for cytomorphological investigation. 185,000 grid images of the annotated regions were used to train a DNN to identify AOI of optimal quality and flag positions for subsequent scanning at 40x magnification. We used a Metasystems (Altlussheim, GER) Metafer System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder SFx80, automated oil disperser) for scanning. To train an object detection (OD) model, we ran the AOI detection network on 19 BM smears which provided 173 FOV (40x) of BM cell layers. Each single cell was marked by human investigators using exact polygon labels on a digital interactive pencil display (Cintiq DTK2260K0A from Wacom). In total, 19,697 cells were labeled. We set up a supervised ML model, using the labeled images as an input. We fine-tuned the imagenet dataset pre-trained Termausnet with UNet16 model with our dataset and applied a postprocessing step for cell level validation instead of the pixel level one. To reduce overfitting, image augmentation algorithms were applied. Then the OD model was used to acquire 156,049 precisely outlined single cell images (without artifact classes) from 8047 FOVs from 147 BM smears, including normal/reactive patterns and all kinds of BM neoplasms. 0 to 248 intact single cells per FOV were captured depending on smear quality. Out of these images, a balanced set of 38,372 images was classified by hem experts, referring to 23 predefined cell types, including signature cell types of different leukemias (e.g. hairy cells, APL cells) as well as tumour cells. The final training consisted of 476 to 29,370 (median: 2,178) cell images per cell type. Using these images as an input, we set up a supervised ML model, outputting predicted probabilities of the 23 pre-defined classes. We used ImageNet-pretrained Xception as our base model. We trained, evaluated and deployed the model using Amazon SageMaker. For the large amount of data, most of the computing process was run in a cloud based web platform. Results: AOI identification DNN was able to detect relevant regions in BM smears in an average of 6 min. Subsequent acquisition of high resolution images from the 50 positions with highest quality values took an average of 1:30 min. The OD DNN was able to identify nucleated cells at 96% sensitivity and 90% specifity in an independent test set of 11 FOV. In order not to miss relevant objects, recall was overweighted over precision (5:1), leaving correction of false positive labels to human reviewers in an assumed digital workflow. For the object classification (OC) of single cells, the 3rd DNN was able to predict the predefined 23 BM cell types at acceptable median accuracy (86%) in an independent test set of 12,798 images. For 7 critical pathological cell forms (blasts, APL cells, hairy cells, lymphoma cells, tumour cells, inv16 eosinophils), median accuracy amounted to 86% as well. Conclusion: Combination of 3 independent DNN provides a fully automated workflow for high throughput hem labs. It includes high resolution scanning of AOI in BM smears and identification and classification of single cells. Overview images (10x magnification) and single cell images (40x magnification with oil) can be reviewed in digital galleries by humans, prioritizing cases with pathological cell types identified by AI. Use of cloud-based platforms is inevitable with regard to huge amounts of processed data. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal