105,025 research outputs found
Kyrgyzstan’s Manas epos millennium celebrations: post-colonial resurgence of Turkic culture and the marketing of cultural tourism
No full textThe paper addresses the symbolic nature of the Manas epos and its influence on both the unification of Kyrgyzstan and the enhancement of the country's national and Turkic identity. The case of the Manas epos millennium celebrations event is then used to illustrate the relationship between the uses of the Manas 'legend' in the construction of a national identity and in the positioning of the cultural tourism product. The paper subsequently assess the potential usefulness of the Manas epos in the creation of a destination image for Kyrgyzstan and in the positioning of Kyrgyzstan in the global tourism marketplace
Bayesian Oracle for bounding information gain in neural encoding models
No full textMany normative theories that link neural population activity to cognitive tasks, such as neural sampling and the Bayesian brain hypothesis, make predictions for single trial fluctuations. Linking information theoretic principles of cognition to neural activity thus requires models that accurately capture all moments of the
response distribution. However, to measure the quality of such models, commonly used correlation-based metrics are not sufficient as they mainly care about the mean of the response distribution. An interpretable alternative evaluation metric for likelihood-based models is Information Gain (IG) which evaluates the likelihood of a model relative to a lower and upper bound. However, while a lower bound is usually easy to obtain and evaluate, constructing an upper bound turns out to be challenging for neural recordings with relatively low numbers of repeated trials, high (shared) variability and sparse responses. In this work, we generalize the jackknife oracle estimator for the mean – commonly used for correlation metrics – to a
flexible Bayesian oracle estimator for IG based on posterior predictive distributions. We describe and address the challenges that arise when estimating the lower and upper bounds from small datasets. We then show that our upper bound estimate is data-efficient and robust even in the case of sparse responses and low signal-tonoise ratio. Finally, we provide the derivation of the upper bound estimator for a variety of common distributions including the state-of-the-art zero-inflated mixture models
Bayesian Oracle for bounding information gain in neural encoding models
No full textMany normative theories that link neural population activity to cognitive tasks, such as neural sampling and the Bayesian brain hypothesis, make predictions for single trial fluctuations. Linking information theoretic principles of cognition to neural activity thus requires models that accurately capture all moments of the
response distribution. However, to measure the quality of such models, commonly used correlation-based metrics are not sufficient as they mainly care about the mean of the response distribution. An interpretable alternative evaluation metric for likelihood-based models is Information Gain (IG) which evaluates the likelihood of a model relative to a lower and upper bound. However, while a lower bound is usually easy to obtain and evaluate, constructing an upper bound turns out to be challenging for neural recordings with relatively low numbers of repeated trials, high (shared) variability and sparse responses. In this work, we generalize the jackknife oracle estimator for the mean – commonly used for correlation metrics – to a
flexible Bayesian oracle estimator for IG based on posterior predictive distributions. We describe and address the challenges that arise when estimating the lower and upper bounds from small datasets. We then show that our upper bound estimate is data-efficient and robust even in the case of sparse responses and low signal-tonoise ratio. Finally, we provide the derivation of the upper bound estimator for a variety of common distributions including the state-of-the-art zero-inflated mixture models
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Iraj Bashiri, The History of the Civil War in Tajikistan. Boston, MA: Academic Studies Press, 2020, 336pp., $119.00 h/b.
No full textTARAMAWOSWOS:000686800000016THE TAJIK CIVIL WAR IN THE WAKE OF THE COLLAPSE OF THE SOVIET UNION caused the loss of
tens of thousands of lives and the displacement of over a million Tajiks, leaving a divided nation and
bitter memories on both sides of the conflict. The civil war also had an extensive impact on regional
politics and the relations of Tajikistan with other Central Asian countries and Russia. This volume
by Iraj Bashiri is one of the few works on this ruinous episode of Tajik history. Readers should not
expect a scholarly tome that covers all the literature and every aspect of the civil war, but rather
Bashiri’s personal account, as reflected in the bibliography, which lists mostly Tajik-language
sources and the author’s previous works
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
The construction of Karen Karnak: The multi-author-function
No full textThis thesis is situated within the comparatively recent developments of Web 2.0 and the emergence of interactive WikiMedia, and explores the mode of authorship within a Read/Write culture compared to that of a Read/Only tradition. The hypothesis of this study is that the role of the audience has become merged with the author, and as such, represents new functions and attributes, distinct from a more conventional concept of authorship, in which the roles of audience and author are more separate. Read/Write and participatory culture, as defined by this study, is focused on collaboration, and includes the influences of D.I.Y. culture, Open-Source practices and the production of text by multiple authors. Multi-authorship presents a re-thinking of several concepts which support the notion of the individual author, since the focus of multi-authorship is not on attribution and ownership of a finished text, but on the continued malleability of a text. Modes of multi-authorship, demonstrated in the use of the pseudonyms Alan Smithee and Karen Eliot, represent declarative authors whose names signify multiple origins, whilst concurrently indicating a distinct body of work. The function of these names form an important context to this study, since primary research involves the construction of an experimental mode of multi-authorship utilising WikiMedia technology and the interaction of thirty nine participants, who are invited to create a body of work under the collective pseudonym Karen Karnak. The data generated by this experiment is analysed using aspects of Michel Foucault's author-function to identify and determine power structures inherent in the WikiMedia context. The interplay of power structures, including concepts such as identity, ownership and the body of work, affect the resulting mode of authorship and contribute to the construction of Karen Karnak, suggesting further areas of research into the emerging multi-author
Learning invariance manifolds of visual sensory neurons
No full textRobust object recognition is thought to rely on neural mechanisms that are selective to
complex stimulus features while being invariant to others (e.g., spatial location or orientation). To better understand biological vision, it is thus crucial to characterize which features
neurons in different visual areas are selective or invariant to. In the past, invariances have
commonly been identified by presenting carefully selected hypothesis-driven stimuli which
rely on the intuition of the researcher. One example is the discovery of phase invariance in
V1 complex cells. However, to identify novel invariances, a data-driven approach is more
desirable. Here, we present a method that, combined with a predictive model of neural
responses, learns a manifold in the stimulus space along which a target neuron’s response
is invariant. Our approach is fully data-driven, allowing the discovery of novel neural invariances, and enables scientists to generate and experiment with novel stimuli along the
invariance manifold. We test our method on Gabor-based neuron models as well as on a
neural network fitted on macaque V1 responses and show that 1) it successfully identifies
neural invariances, and 2) disentangles invariant directions in the stimulus space
Learning invariance manifolds of visual sensory neurons
No full textRobust object recognition is thought to rely on neural mechanisms that are selective to
complex stimulus features while being invariant to others (e.g., spatial location or orientation). To better understand biological vision, it is thus crucial to characterize which features
neurons in different visual areas are selective or invariant to. In the past, invariances have
commonly been identified by presenting carefully selected hypothesis-driven stimuli which
rely on the intuition of the researcher. One example is the discovery of phase invariance in
V1 complex cells. However, to identify novel invariances, a data-driven approach is more
desirable. Here, we present a method that, combined with a predictive model of neural
responses, learns a manifold in the stimulus space along which a target neuron’s response
is invariant. Our approach is fully data-driven, allowing the discovery of novel neural invariances, and enables scientists to generate and experiment with novel stimuli along the
invariance manifold. We test our method on Gabor-based neuron models as well as on a
neural network fitted on macaque V1 responses and show that 1) it successfully identifies
neural invariances, and 2) disentangles invariant directions in the stimulus space
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