Topic
Layer (object-oriented design)
About: Layer (object-oriented design) is a research topic. Over the lifetime, 5855 publications have been published within this topic receiving 44859 citations.
Papers published on a yearly basis
Papers
23 Aug 2020
TL;DR: This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
Abstract: Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
1,484 citations
Posted Content•
TL;DR: Universal Dependencies as mentioned in this paper is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework, which consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers.
Abstract: Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.
508 citations
Proceedings Article•
18 May 2021TL;DR: Wang et al. as mentioned in this paper proposed a novel Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting, which can effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, treated for different time periods in parallel.
Abstract: Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks usually utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. Furthermore, existing methods were out at elbows when solving complicated spatial-temporal data: they usually utilize separate modules for spatial and temporal correlations, or they only use independent components capturing localized or global heterogeneous dependencies. To overcome those limitations, our paper proposes a novel Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. First, a data-driven method of generating “temporal graph” is proposed to compensate several genuine correlations that spatial graph may not reflect. STFGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, treated for different time periods in parallel. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer parallelly, STFGNN could handle long sequences by learning more spatial-temporal dependencies with layers stacked. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.
472 citations
Patent•
10 Jan 2003TL;DR: In this paper, the authors present a servlet container that executes a Web tier (304), typically the presentation layer of a given Java-based application, where necessary, the edge layer communicates with code running on an origin server to respond to a given request.
Abstract: According to the invention, application developers separate their Web application into two layers: a highly distributed edge layer and a centralized origin layer. In a representative embodiment, the edge layer supports a servlet container that executes a Web tier (304), typically the presentation layer of a given Java-based application. Where necessary, the edge layer communicates with code running on an origin server to respond to a given request.
444 citations
Patent•
2 Jun 1987
TL;DR: In this article, a system responsive to coordinate information for automatically providing a three-dimensional physical model of a desired geometry and comprising apparatus for selectably solidifying a solidifiable material on a sequential layer by layer basis characterized in that following selectable solidification of a given layer, the non-solidified portions thereof are removed and replaced by a removable support material which is not solidifiable under the same conditions as the solidifiable materials.
Abstract: A system responsive to coordinate information for automatically providing a three-dimensional physical model of a desired geometry and comprising apparatus for selectably solidifying a solidifiable material on a sequential layer by layer basis characterized in that following selectable solidification of a given layer, the non-solidified portions thereof are removed and replaced by a removable support material which is not solidifiable under the same conditions as the solidifiable material.
392 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 1 |
| 2021 | 181 |
| 2020 | 431 |
| 2019 | 656 |
| 2018 | 636 |
| 2017 | 500 |