Classes Of Artifacts Research Articles

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

Impact of oscillometric measurement artefacts in ambulatory blood pressure monitoring on estimates of average blood pressure and of its variability: a pilot study.

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. Four classes of artefacts have been detected: motor activity artefacts, cuff errors, cardiovascular arousals, and arrhythmias. The data consisted of uncorrected measurements (all data), corrected measurements (all artefact free data), and artefact affected data. A total of 30 individuals (9 female/21 male), aged between 36 and 86 years, mean: 65.5 (standard deviation: 9.5) were included in the study. The 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. Nocturnal measurements showed no artefact-depended differences. Individual comparisons yielded that 23% of the participants crossed the threshold for BP classification for either 24-h, daytime or nocturnal hypertension when comparing uncorrected and artefact-free measurements. Dipping classification changed within 24% of participants. BP variability was 21 and 12% higher for SPB and DBP, respectively, during daytime. These differences were even higher (27% for SBP and 21% for DPB) during night-time. 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.

Relating tools to tasks: shell hammers and oyster management on Florida's northern Gulf Coast

ABSTRACT The purposeful management of oyster fisheries has increasingly been used to explain millennial-scale sustainability evident at shell-bearing archaeological sites throughout the Southeast and beyond. While the focus of oyster management has been on the oysters themselves, the tools related to sustainable practices must also be the subject of investigation. Crown conch hammers are common artifacts recovered from coastal sites in the Southeast although their purpose has remained ambiguous. Building on previous studies of this class of artifacts, this paper relates crown conch hammers to oyster processing, specifically the management practice of culling. Evidence presented herein was recovered from the Lower Suwannee region of the northern Gulf Coast of Florida.

Ice crystal images from optical array probes: classification with convolutional neural networks

Abstract. Although airborne optical array probes (OAPs) have existed for decades, our ability to maximize extraction of meaningful morphological information from the images produced by these probes has been limited by the lack of automatic, unbiased, and reliable classification tools. The present study describes a methodology for automatic ice crystal recognition using innovative machine learning. Convolutional neural networks (CNNs) have recently been perfected for computer vision and have been chosen as the method to achieve the best results together with the use of finely tuned dropout layers. For the purposes of this study, The CNN has been adapted for the Precipitation Imaging Probe (PIP) and the 2DS Stereo Probe (2DS), two commonly used probes that differ in pixel resolution and measurable maximum size range for hydrometeors. Six morphological crystal classes have been defined for the PIP and eight crystal classes and an artifact class for the 2DS. The PIP and 2DS classifications have five common classes. In total more than 8000 images from both instruments have been manually labeled, thus allowing for the initial training. For each probe the classification design tries to account for the three primary ice crystal growth processes: vapor deposition, riming, and aggregation. We included classes such as fragile aggregates and rimed aggregates with high intra-class shape variability that are commonly found in convective clouds. The trained network is finally tested through human random inspections of actual data to show its real performance in comparison to what humans can achieve.

Destinations in Mind: Portraying Places on the Roman Empire’s Souvenirs By Kimberly Cassibry. Oxford: Oxford University Press 2021. Pp. xvii + 278. $99. ISBN 9780190921897 (cloth).

<i>Destinations in Mind: Portraying Places on the Roman Empire’s Souvenirs</i> By Kimberly Cassibry. Oxford: Oxford University Press 2021. Pp. xvii + 278. $99. ISBN 9780190921897 (cloth).

How Statues Speak

Abstract We apply a familiar distinction from philosophy of language to a class of material artifacts that are sometimes said to “speak”: statues. By distinguishing how statues speak at the locutionary level versus at the illocutionary level, or what they say versus what they do, we obtain the resource for addressing two topics. First, we can explain what makes statues distinct from street art. Second, we can explain why it is mistaken to criticize—or to defend—the continuing presence of statues based only on what they represent. Both explanations are driven by the same core idea: the significance of statues arises primarily from what they do and not what they say.

Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito

The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take multiple hours spread over several days to complete. Because of this, microdebitage analysis is typically completed in very small areas of sites due to the intensive time and labor commitment required. Recently, however, my colleagues and I have developed a novel, interdisciplinary method that combines dynamic image analysis and machine learning to analyze microdebitage taken from soil samples at archaeological sites. Analyses of experimental microdebitage demonstrated that microdebitage could be accurately and efficiently identified within archaeological soil samples using this method. In the present study, we apply these methods to soil samples taken from the Maya Capital of Tamarindito in Guatemala to verify whether these methods remain accurate when applied to archaeological contexts.

Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors

The two interferometric LIGO gravitational-wave observatories provide the most sensitive data to date to study the gravitational-wave universe. As part of a global network, they have completed their third observing run in which they observed many tens of signals from merging compact binary systems. It has long been known that a limiting factor in identifying transient gravitational-wave signals is the presence of transient non-Gaussian noise, which reduce the ability of astrophysical searches to detect signals confidently. Significant efforts are taken to identify and mitigate this noise at the source, but its presence persists, leading to the need for software solutions. Taking a set of transient noise artefacts categorised by the GravitySpy software during the O3a observing era, we produce parameterised population models of the noise projected into the space of astrophysical model parameters of merging binary systems. We compare the inferred population properties of transient noise artefacts with observed astrophysical systems from the GWTC2.1 catalogue. We find that while the population of astrophysical systems tend to have near equal masses and moderate spins, transient noise artefacts are typically characterised by extreme mass ratios and large spins. This work provides a new method to calculate the consistency of an observed candidate with a given class of noise artefacts. This approach could be used in assessing the consistency of candidates found by astrophysical searches (i.e. determining if they are consistent with a known glitch class). Furthermore, the approach could be incorporated into astrophysical searches directly, potentially improving the reach of the detectors, though only a detailed study would verify this.

Isometric artifacts from polymerase chain reaction-massively parallel sequencing analysis of short tandem repeat loci: An emerging issue from a new technology?

The recent introduction of polymerase chain reaction (PCR)‐massively parallel sequencing (MPS) technologies in forensics has changed the approach to allelic short tandem repeat (STR) typing because sequencing cloned PCR fragments enables alleles with identical molecular weights to be distinguished based on their nucleotide sequences. Therefore, because PCR fidelity mainly depends on template integrity, new technical issues could arise in the interpretation of the results obtained from the degraded samples. In this work, a set of DNA samples degraded in vitro was used to investigate whether PCR‐MPS could generate “isometric drop‐ins” (IDIs; i.e., molecular products having the same length as the original allele but with a different nucleotide sequence within the repeated units). The Precision ID GlobalFiler NGS STR panel kit was used to analyze 0.5 and 1 ng of mock samples in duplicate tests (for a total of 16 PCR‐MPS analyses). As expected, several well‐known PCR artifacts (such as allelic dropout, stutters above the threshold) were scored; 95 IDIs with an average occurrence of 5.9 IDIs per test (min: 1, max: 11) were scored as well. In total, IDIs represented one of the most frequent artifacts. The coverage of these IDIs reached up to 981 reads (median: 239 reads), and the ratios with the coverage of the original allele ranged from 0.069 to 7.285 (median: 0.221). In addition, approximately 5.2% of the IDIs showed coverage higher than that of the original allele. Molecular analysis of these artifacts showed that they were generated in 96.8% of cases through a single nucleotide change event, with the C > T transition being the most frequent (85.7%). Thus, in a forensic evaluation of evidence, IDIs may represent an actual issue, particularly when DNA mixtures need to be interpreted because they could mislead the operator regarding the number of contributors. Overall, the molecular features of the IDIs described in this work, as well as the performance of duplicate tests, may be useful tools for managing this new class of artifacts otherwise not detected by capillary electrophoresis technology.

Ancient Maya Embedded Economies and Changing Ground Stone Densities in Households at Actuncan, Belize

In embedded economies where multiple modes of production and exchange exist, artifact distributions in households alone do not reflect the strength of specific modes. We use a diachronic perspective tied to changes in political economies and artifact class densities standardized by excavation volume at the Maya site of Actuncan, Belize, to elucidate changes in the strength of individual production and exchange modes in the Preclassic and Classic periods. We focus on ground stone densities as a measure of grinding intensity across elite and common households. Data indicate that common households always ground more maize than elites, but intensity peaked in the Late Classic when tax and tribute demands and market exchanges were greatest. In the Terminal Classic, common household grinding intensity decreased by half as tribute burdens diminished, illustrating the impact of political hierarchies on household economies.

The Mycenaean Settlement on Tsoungiza Hill. By James C. Wright and Mary K. Dabney (Nemea Valley Archaeological Project 3). Princeton: American School of Classical Studies at Athens 2020. Pp. 1240. $150. ISBN 978-0-87661-924-7 (cloth).

<i>The Mycenaean Settlement on Tsoungiza Hill</i>. By James C. Wright and Mary K. Dabney (Nemea Valley Archaeological Project 3). Princeton: American School of Classical Studies at Athens 2020. Pp. 1240. $150. ISBN 978-0-87661-924-7 (cloth).

Combined beam hardening artifact correction and quantitative microanalysis of colloidal depositions in deep bed filtration experiments investigated by 3D X-ray computed microtomography

In this study we present quantitative X-ray computed microtomography measurements (μCT) of retained sub-micron-sized particles in open porous media carried out in a laboratory μCT setup. Due to the polychromatic spectrum of the used X-rays, the tomograms are affected by various non-linear artifacts, which belong to the class of beam hardening artifacts. These artifacts become more dominant, when the amount of retained particles increases and can affect wide areas of the images, making a qualitative and quantitative analysis barely possible. Furthermore, the colloidal depositions show an inhomogeneous distribution inside the filter, making a reliable material discrimination between filter and particle material challenging. We introduce a calibration method, which is capable to sufficiently remove the majority of the artifacts by linearization of the projection data and thus enabling the precise material quantification of the retained colloids in the reconstructed tomograms. While most beam hardening correction routines are only applicable to homogeneous materials, our algorithm takes into account inhomogeneous material distributions and is adapted to multi-material systems. Moreover, the method includes a material discrimination of the colloids and the filter in the raw data domain. Thus, erroneous segmentations at the interfaces between different material fractions are avoided. As a result we present quantitative concentration maps of the particle distribution inside the porous media with a resolution of < 10μm. A series of validation samples was prepared, covering a wide range of different, representative filter loading stages. The accuracy of the particle quantification was evaluated from these samples and the relative deviation of the overall contained particle mass was less than 10% in all cases, partially even less than 1%. The overall image quality due to the artifact removal was significantly improved. The local variation of the particle concentration could be well assessed from the obtained concentration maps.

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks.

Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification (“poor” vs. “good” quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or “slabs” extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.

Levallois technology in southern Patagonia (Argentina and Chile): current knowledge and future perspectives

Situated on the extreme point of South America, southern Patagonia has yielded the southernmost evidence of Levallois lithic technology. There, enough evidence of the use of a method of core preparation for production of predefined flakes similarly structured to the known Levallois technology (LT) in the Old World is present. An overview of the currently available information (distribution, chronology, frequency, artifact classes, raw materials and techno-morphological attributes) on Levallois technology in southern continental Patagonia and on the Isla Grande of Tierra del Fuego (Argentina and Chile) is presented here in order to discuss its place in the shared technological background of local hunter-gatherers during the Holocene.&#x0D; The analysis of the information shows that the LT occurs at very low frequency, mainly involving cores, knives and sidescrapers on local raw materials but encompassing a high diversity in terms of ecological contexts and human subsistence. These findings are an indicator that several populations were simultaneously familiar with this technology, possibly through long-distance movements of individuals or social networks to share ideas and information on how to make and use these artifacts. In this regard, the middle Holocene was an important period when the LT became a shared technological phenomenon on a macro-regional scale. However, the evidence on LT is still scarce in Patagonian contexts and emphasizes possible linkages with other reduction strategies, especially the Discoid flaking method, the search for maximize lithic raw material exploitation and even the need to produce versatile tools to deal with a diversity of tasks. It is still unclear therefore whether the LT reflects a truly method to get predetermined flakes with specific morphological features or it mainly attended to other circumstances due to the influence of environment, the spatial organization of human groups or the lithic reduction systems. This issue probably requires an exhaustive study of Levallois cores, specially their own particular sequential development of reduction, frequency and relationship with other knapping techniques recorded in the Patagonian lithic assemblages.

The Grave of the Griffin Warrior at Pylos: Construction, Burial, and Aftermath

The circumstances of the discovery, stratigraphy, and construction of the grave of the Griffin Warrior were described briefly in 2016 as an introduction to a detailed presentation of the four gold rings found inside it. Here the "life history" of the grave is considered fully. As a sealed context, the grave of the Griffin Warrior also provides a key dated context for many classes of artifact, the chronologies of which have not hitherto been well defined. Because of its importance in this regard, supporting evidence is presented that the Griffin Warrior was buried in Late Helladic IIA and that the burial deposit in the grave lay undisturbed after Late Helladic IIA.

Classification and identification of glass artifacts based on high-dimensional clustering and XGBoost algorithm

After the weathering of glass artifacts, a large number of environmental elements are exchanged with elements inside the glass artifacts. In this paper, based on the chemical composition of the artifact samples and other detection means, they are classified into two types: lead-barium glass and high-potassium glass. A sub-classification model based on high-dimensional clustering is introduced, and an identification model is established by optimizing the K-means algorithm. Finally, a feature classification model based on XGBoost algorithm is used to analyze the chemical composition of the unknown class of glass artifacts.

Technoscience, Humanity and Man

This article is a reflection on some conceptual ideas of the book “Humanity and Technos: the philosophy of coevolution” related to negative assessments of the socio-anthropological consequences of the development of technoscience. It is noted that the author’s criticism of novationist interpretations of scientific and technological progress is subordinated to the main idea of the book about the incompatibility of the principles of convergence and coevolution in the relationship between man and technology. It is shown that the book examines not only the external challenges of technoscience, but also the areas of scientific research that threatens the world of traditional man. Based on the results of the analysis of the book's content the following main conclusions are made. 1. Despite the presence of a large group of social problems generated by the fourth technological order the authors of the book are most concerned about the consequences of the development of digital technologies that can change the man's generic nature. 2. Rhetorical theory of number considering in the book as a worldview paradigm of the digital revolution actually has a narrower subject specificity. 3. The assumption that exist a parallel evolution of technology independent of human is contradicting the original premise about the artificial nature of artifacts. 4. Conclusion about the presence of animal traits in humans that were lost during technological revolution is requires specification. 5. The problem of an existential crisis caused by the appearance of a new class of technical artifacts and also the theme of the deficit of freedom in the virtual space has not been its developed. 6. The project of the ideology of left conservatism positioned as the most consistent variant of the philosophy of resistance to the “technogenic degradation of mankind” seems to contradict the dialectical-revolutionary essence of classical Marxism. In conclusion the ideological sense of the main goal for humanity proposed by the authors and the relativity of the ideology of conservatism are shown.

Classification and identification of glass artifacts based on high-dimensional clustering and XGBoost algorithm

After the weathering of glass artifacts, a large number of environmental elements are exchanged with elements inside the glass artifacts. In this paper, based on the chemical composition of the artifact samples and other detection means, they are classified into two types: lead-barium glass and high-potassium glass. A sub-classification model based on high-dimensional clustering is introduced, and an identification model is established by optimizing the K-means algorithm. Finally, a feature classification model based on XGBoost algorithm is used to analyze the chemical composition of the unknown class of glass artifacts.