5,676 research outputs found

    Gender and Medieval archaeology: storming the castle

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    Despite feminist critiques over three decades, Archaeological scholarship remains predominantly focused on exploring patriarchal narratives and is thereby complicit in reinforcing structural inequalities. Questions must be asked of how the construction of archaeological knowledge affects representation and impacts on our ‘archaeologies’. This paper explores the relative absence of gendered approaches within Archaeology through the lens of Later Medieval Archaeology in particular, and with a micro-focus on Castle-studies in Britain and Ireland. Is there a reason for the silence in relation to gender in the archaeology of the later middle ages

    Improving the vector auto regression technique for time-series link prediction by using support vector machine

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    Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR) technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM) to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN), Adamic-Adar (AA), Jaccard’s Coefficient (JC), Preferential Attachment (PA), and Research Allocation Index (RA). A DBLP dataset covering the years from 2003 until 2013 was collected and transformed into time-sliced graph representations. The appropriate matrices were computed from these graphs, mapped to the feature space, and then used to build baseline VAR models with lag of 2 and some corresponding SVM classifiers. Using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) as the main fitness metric, the average result of 82.04% for the VAR was improved to 84.78% with SVM. Additional experiments to handle the highly imbalanced dataset by oversampling with SMOTE and undersampling with K-means clusters, however, did not improve the average AUC-ROC of the baseline SVM

    Marriage and the crisis of peasant society in Gujarat, India

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    This contribution takes marriage as the example of a crisis of production and reproduction in rural India. Through the juxtaposition of ethnography separated by six decades, we detail a shift away from land and agriculture as the primary markers of status among the Patidars of central Gujarat, western India, in favour of a hierarchical understanding of international migration. The paper discusses the disconnect between a cultural revolution in favour of migration, and the failure of many to live up to their own cultural standards. More broadly, we reflect on the forces that simultaneously strengthen and dissolve caste inequality in the context of India's uneven growth

    Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

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    Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal for structured stimuli is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI

    Journal of African Christian Biography: v. 4, no. 1

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    A publication of the Dictionary of African Christian Biography with U.S. offices located at the Center for Global Christianity and Mission at Boston University. This issue focuses on: 1. Introducing African Christian Biography. 2. Modern African Church History and the Streetlight Effect. 3. Both African and Christian. 4. Musicians and Composers in African Christianity. 5. Yared. 6. John Knox Bokwe. 7. Recent Print and Digital Resources Related to Christianity in Africa. 8. Guidelines for Article Contributors. 9. Suggested Interview Guidelines and Questions. 10. Guidelines for Book Reviewers

    The Interscutularis Muscle Connectome

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    The complete connectional map (connectome) of a neural circuit is essential for understanding its structure and function. Such maps have only been obtained in Caenorhabditis elegans. As an attempt at solving mammalian circuits, we reconstructed the connectomes of six interscutularis muscles from adult transgenic mice expressing fluorescent proteins in all motor axons. The reconstruction revealed several organizational principles of the neuromuscular circuit. First, the connectomes demonstrate the anatomical basis of the graded tensions in the size principle. Second, they reveal a robust quantitative relationship between axonal caliber, length, and synapse number. Third, they permit a direct comparison of the same neuron on the left and right sides of the same vertebrate animal, and reveal significant structural variations among such neurons, which contrast with the stereotypy of identified neurons in invertebrates. Finally, the wiring length of axons is often longer than necessary, contrary to the widely held view that neural wiring length should be minimized. These results show that mammalian muscle function is implemented with a variety of wiring diagrams that share certain global features but differ substantially in anatomical form. This variability may arise from the dominant role of synaptic competition in establishing the final circuit.National Institutes of Health (U.S.

    Conditional t-SNE : more informative t-SNE embeddings

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    Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provided new insights into real data

    Where does axon guidance lead us?

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    During neural circuit formation, axons need to navigate to their target cells in a complex, constantly changing environment. Although we most likely have identified most axon guidance cues and their receptors, we still cannot explain the molecular background of pathfinding for any subpopulation of axons. We lack mechanistic insight into the regulation of interactions between guidance receptors and their ligands. Recent developments in the field of axon guidance suggest that the regulation of surface expression of guidance receptors comprises transcriptional, translational, and post-translational mechanisms, such as trafficking of vesicles with specific cargos, protein-protein interactions, and specific proteolysis of guidance receptors. Not only axon guidance molecules but also the regulatory mechanisms that control their spatial and temporal expression are involved in synaptogenesis and synaptic plasticity. Therefore, it is not surprising that genes associated with axon guidance are frequently found in genetic and genomic studies of neurodevelopmental disorders

    Selected Papers from “Theory of Hadronic Matter under Extreme Conditions”

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    The book is devoted to the discussion of modern aspects of the theory of hadronic matter under extreme conditions. It consists of 12 selected contributions to the second international workshop on this topic held in fall 2019 at JINR Dubna, Russia. Of particular value are the contributions to lattice gauge theory studies attacking the problem of simulating QCD at finite baryon densities, one of the major challenges at the present time in this field. Another unique aspect is provided by the discussion of puzzling effects that appear in the poduction of hadrons in nuclear collisions, like the horn in the K+/pi+ ratio, which are subject to hydrodynamic and reaction-kinetic modeling of these nonequilibrium phenomena

    Agency and the \u3cem\u3eAdagio\u3c/em\u3e: Mimetic Engagement in Barber\u27s Op. 11 Quartet

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    Samuel Barber’s Adagio for Strings (1936) is undoubtedly the most famous elegiac work of the twentieth-century. We know it from movies, television, and highly publicized memorial services. Yet the music was originally written as the second movement of Barber’s string quartet, op. 11, with a number of interesting connections to the outer movements. This article highlights several recurring gestures throughout op. 11 that suggest the will of an individual “agent” struggling against gravity and weight. It proposes a broad, multi-movement narrative that draws together the three movements with a special focus on mimetic engagement, leading-tone resolution, and the quest for major-mode closure
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