1,721,044 research outputs found
L’esperienza del controllo delle ICPA nelle realtà regionali e locali: l’esperienza di Sassari
No full textFlexible acceleration of convolutions on FPGAs: Planning NEURAghe 2.0
No full textConvolutional Neural Networks are commonly employed in applications involving Computer Vision tasks like image/video classification/recognition/segmentation. The increasing focus of the community on this topic, has generated a wide scope of approaches that use different kernel shapes and techniques for executing convolutions with respect to the classic one, such as for example separable convolutions, deformable convolutions or deconvolutions ([4, 5]), frequently used in semantic segmentation tasks ([23, 13]). While it is common knowledge that FPGAs can be used to accelerate classic Convolutional layers in CNNs, there is limited literature about FPGA-based accelerators supporting less regular and common processing kernels ([20]). In our research, starting from the previous experience acquired developing NEURAghe, we plan to improve flexibility of CNN accelerators and to study new methodologies to improve efficiency on the previously mentioned use-cases. As a first experiment we focus on layered approaches based on 1D convolutions, that, as indicated by several recent research results, can be effectively used to classify and segment time series and sequences, as well as in tasks involving sequence modeling. In multiple scenarios a convolution approach applied on the time dimension, hereafter called Temporal Convolution Network (TCN) can outperform classic strategies relying on recurrent networks in terms of accuracy and training time. We modified NEURAghe to support TCN and validate results on an ECG-classification benchmark, achieving up to 95% efficiency in terms of GOPS/s with respect to the accelerator peak performance
Optimizing Temporal Convolutional Network Inference on FPGA-Based Accelerators
Full text linkConvolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition and segmentation. Recent research results demonstrate that multi-layer (deep) network involving mono-dimensional convolutions and dilation can be effectively used in time series and sequences classification and segmentation, as well as in tasks involving sequence modeling. These structures, commonly referred to as Temporal Convolutional Networks (TCNs), represent an extremely promising alternative to recurrent architectures, commonly used across a broad range of sequence modeling tasks. While FPGA based inference accelerators for classic CNNs are widespread, literature is lacking in a quantitative evaluation of their usability on inference for TCN models. In this paper we present such an evaluation, considering a CNN accelerator with specific features supporting TCN kernels as a reference and a set of state-of-the-art TCNs as a benchmark. Experimental results show that, during TCN execution, operational intensity can be critical for the overall performance. We propose a convolution scheduling based on batch processing that can boost efficiency up to 96% of theoretical peak performance. Overall we can achieve up to 111,8 GOPS/s and a power efficiency of 33,8 GOPS/s/W on an Ultrascale+ ZU3EG (up to 10× speedup and 3× power efficiency improvement with respect to pure software implementation)
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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