1,720,989 research outputs found
Graph Neural Networks for High-Level Synthesis Design Space Exploration
No full textHigh-level Synthesis (HLS) Design-Space Exploration (DSE) aims at identifying Pareto-optimal synthesis configurations whose exhaustive search is unfeasible due to the design-space dimensionality and the prohibitive computational cost of the synthesis process. Within this framework, we address the design automation problem by proposing graph neural networks that jointly predict acceleration performance and hardware costs of a synthesized behavioral specification given optimization directives. Learned models can be used to rapidly approach the Pareto curve by guiding the DSE, taking into account performance and cost estimates. The proposed method outperforms traditional HLS-driven DSE approaches, by accounting for the arbitrary length of computer programs and the invariant properties of the input. We propose a novel hybrid control and dataflow graph representation that enables training the graph neural network on specifications of different hardware accelerators. Our approach achieves prediction accuracy comparable with that of state-of-the-art simulators without having access to analytical models of the HLS compiler. Finally, the learned representation can be exploited for DSE in unexplored configuration spaces by fine-tuning on a small number of samples from the new target domain. The outcome of the empirical evaluation of this transfer learning shows strong results against state-of-the-art baselines in relevant benchmarks
Crashworthiness assessment of a composite fuselage stanchion employing a strain rate dependent damage model
Full text linkNumerical testing is crucial for the design of composite fuselages, which have strict crashworthiness regulations. However, the majority of studies on numerical fuselage impacts do not account for the effects of strain rate in simulations. A damage model considering strain rate dependence has been implemented to accurately predict the impact behaviour of a composite fuselage structure. This model enhances the existing three-dimensional Hashin criterion by incorporating strain rate effects and its implemented numerically using a VUMAT subroutine in ABAQUS/explicit. Validation of the model is done through a low-velocity impact problem, showing a better correlation with experimental data compared to previous numerical analyses available in the literature. The study focuses on high-energy impact on a composite stanchion in the lower lobe of an aircraft fuselage. Results demonstrate that the newly proposed model effectively predicts failure zones and modes, indicating its potential in addressing dynamic composite problems typical of impact scenarios
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Full text linkModeling multivariate time series as temporal signals over a (possibly
dynamic) graph is an effective representational framework that allows for
developing models for time series analysis. In fact, discrete sequences of
graphs can be processed by autoregressive graph neural networks to recursively
learn representations at each discrete point in time and space. Spatiotemporal
graphs are often highly sparse, with time series characterized by multiple,
concurrent, and long sequences of missing data, e.g., due to the unreliable
underlying sensor network. In this context, autoregressive models can be
brittle and exhibit unstable learning dynamics. The objective of this paper is,
then, to tackle the problem of learning effective models to reconstruct, i.e.,
impute, missing data points by conditioning the reconstruction only on the
available observations. In particular, we propose a novel class of
attention-based architectures that, given a set of highly sparse discrete
observations, learn a representation for points in time and space by exploiting
a spatiotemporal propagation architecture aligned with the imputation task.
Representations are trained end-to-end to reconstruct observations w.r.t. the
corresponding sensor and its neighboring nodes. Compared to the state of the
art, our model handles sparse data without propagating prediction errors or
requiring a bidirectional model to encode forward and backward time
dependencies. Empirical results on representative benchmarks show the
effectiveness of the proposed method.Comment: Accepted at NeurIPS 202
Sparse Graph Learning from Spatiotemporal Time Series
Full text linkOutstanding achievements of graph neural networks for spatiotemporal time
series analysis show that relational constraints introduce an effective
inductive bias into neural forecasting architectures. Often, however, the
relational information characterizing the underlying data-generating process is
unavailable and the practitioner is left with the problem of inferring from
data which relational graph to use in the subsequent processing stages. We
propose novel, principled - yet practical - probabilistic score-based methods
that learn the relational dependencies as distributions over graphs while
maximizing end-to-end the performance at task. The proposed graph learning
framework is based on consolidated variance reduction techniques for Monte
Carlo score-based gradient estimation, is theoretically grounded, and, as we
show, effective in practice. In this paper, we focus on the time series
forecasting problem and show that, by tailoring the gradient estimators to the
graph learning problem, we are able to achieve state-of-the-art performance
while controlling the sparsity of the learned graph and the computational
scalability. We empirically assess the effectiveness of the proposed method on
synthetic and real-world benchmarks, showing that the proposed solution can be
used as a stand-alone graph identification procedure as well as a graph
learning component of an end-to-end forecasting architecture.Comment: Accepted for publication in JML
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
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