1,720,981 research outputs found
Double Graph Attention Networks for Visual Semantic Navigation
No full textArtificial Intelligence (AI) based on knowledge graphs has been invested in realizing human intelligence like thinking, learning, and logical reasoning. It is a great promise to make AI-based systems not only intelligent but also knowledgeable. In this paper, we investigate knowledge graph based visual semantic navigation using deep reinforcement learning, where an agent reasons actions against targets specified by text words in indoor scenes. The agent perceives its surroundings through egocentric RGB views and learns via trial-and-error. The fundamental problem of visual navigation is efficient learning across different targets and scenes. To obtain an empirical model, we propose a spatial attention model with knowledge graphs, DGVN, which combines both semantic information about observed objects and spatial information about their locations. Our spatial attention model is constructed based on interactions between a 3D global graph and local graphs. The two graphs we adopted encode the spatial relationships between objects and are expected to guide policy search effectively. With the knowledge graph and its robust feature representation using graph convolutional networks, we demonstrate that our agent is able to infer a more plausible attention mechanism for decision-making. Under several experimental metrics, our attention model is shown to achieve superior navigation performance in the AI2-THOR environment.</p
An optimal algorithm for stochastic matroid bandit optimization
No full textThe selection of leaders in leader-follower multi-agent systems can be naturally formulated as a matroid optimization problem. In this paper, we investigate the online and stochastic version of such a problem, where in each iteration or round, we select a set of leaders and then observe a random realization of the corresponding reward, i.e., of the system performance. This problem is referred to as a stochastic matroid bandit, a variant of combinatorial multi-armed bandit problems where the underlying combinatorial structure is a matroid. We consider semi-bandit feedback and Bernoulli rewards, and derive a tight and problem-dependent lower bound on the regret of any consistent algorithm. We propose KL-OSM, a computationally efficient algorithm that exploits the matroid structure. We derive a finite-time upper bound of the regret of KL-OSM that improves the performance guarantees of existing algorithms. This upper bound actually matches our lower bound, i.e., KL-OSM is asymptotically optimal. Numerical experiments attest that KL-OSM outperforms state-of-the-art algorithms in practice, and the difference in some cases is significant. </p
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
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions?
Get PDFWe consider the situation when a learner faces a set of unknown discrete distributions defined over a common alphabet , and can build for each distribution an individual high-probability confidence set thanks to observations sampled from . The set is structured: each distribution is obtained from the same common, but unknown, distribution q via applying an unknown permutation to . We call this \emph{permutation-equivalence}. The goal is to build refined confidence sets \emph{exploiting} this structural property. Like other popular notions of structure (Lipschitz smoothness, Linearity, etc.) permutation-equivalence naturally appears in machine learning problems, and to benefit from its potential gain calls for a specific approach. We present a strategy to effectively exploit permutation-equivalence, and provide a finite-time high-probability bound on the size of the refined confidence sets output by the strategy. Since a refinement is not possible for too few observations in general, under mild technical assumptions, our finite-time analysis establish when the number of observations are large enough so that the output confidence sets improve over initial individual sets. We carefully characterize this event and the corresponding improvement. Further, our result implies that the size of confidence sets shrink at asymptotic rates of and , respectively for elements inside and outside the support of q, when the size of each individual confidence set shrinks at respective rates of and . We illustrate the practical benefit of exploiting permutation equivalence on a reinforcement learning task
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