1,721,006 research outputs found
Effective Maintenance of Cell Microenvironment for Long-term Neuronal Culture in Nano-Liter Scale Microfluidic Channels
No full textSingle view learning in action recognition
No full textViewpoint is an essential aspect of how an action is visually perceived, with the motion appearing substantially different for some viewpoint pairs. Data driven action recognition algorithms compensate for this by including a variety of viewpoints in their training data, adding to the cost of data acquisition as well as training. We propose a novel methodology that leverages deeply pretrained features to learn actions from a single viewpoint using domain adaptation for knowledge transfer. We demonstrate the effectiveness of this pipeline on 3 different datasets: IXMAS, MoCA and NTU RGBD+, and compare with both classical and deep learning methods. Our method requires low training data and demonstrates unparalleled cross-view action recognition accuracies for single view learning
Adversarial feature refinement for cross-view action recognition
No full textApparent motion information of an action may vary dramatically from one view to another, making transfer of knowledge across views a core challenge of action recognition. Recent times have seen the use of large scale datasets to compensate for this lack in generalization, and in fact most state-of-the-art methods today require large amounts of training data and have high computational cost while training. We propose a novel technique leveraging pre-trained features refined to minimize the view-related information through adversarial training inspired by domain adaptation methods. Our method is able to recognize actions from unfamiliar viewpoints and works effectively on substantially less training data than the ones necessary to train state-of-the-art cross-view methods with exceptional results
Cross-view action recognition with small-scale datasets
No full textCross-view action recognition refers to the task of recognizing actions observed from view-points that are unfamiliar to the system. To address the complexity of the problem, state of the art methods often rely on large-scale datasets, where the variability of viewpoints is appropriately represented. However, this comes to a significant price, in terms of computational power, time, costs, energy for both gathering data annotation and training the model. We propose a methodological pipeline that tackles the same challenges with specific focus on small-scale datasets and attention to the amount of resources required. The core idea of our method is to transfer knowledge from an intermediate, pre-trained representation, under the hypothesis that it already may implicitly incorporate relevant cues for the task. We rely on an effective domain adaptation strategy coupled with the design of a robust classifier that promotes view-invariant properties and allows us to efficiently generalise to action recognition to unseen viewpoints. In contrast to other state-of-art methods employing also alternative data modalities, our approach is purely video-based and thus has a wider field of applications. We present a thorough experimental analysis justifying the choices on the design of the pipeline, and providing a comparison with existing approaches in the two main scenarios of one-one learning and multiple view learning, where our approach provides superior performance
Design and characterization of in vitro neuronal micro-cluster network on microelectrode array
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
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