Space-Time-Blockcodes (STBC), that have been introduced by Alamouti [1] and Tarokh et.al. [2], are now considered in the current 3GPP standard as one method to achieve spatial diversity. While the performance of space-time blockcodes has been subject of extensive research [3, 4, 2], the impact of channel estimation errors and closed loop power control on the performance of of STBC remain open f...

In this work we take a view of syntactic analysis as processing ‘raw’, running text instead of idealised, pre-segmented inputs—a task we dub document parsing. We observe the state of the art in sentence boundary detection and tokenisation, and their effects on syntactic parsing (for English), observing that common evaluation metrics are ill-suited for the comparison of an ‘end-to-end’ syntactic...

Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based attention, dot product by the dense vectors two nodes. This may cause c...

• A novel supergraph data structure to segregate a heterogeneous graph into interconnected, semantically-coherent subgraphs for efficient learning. new representation learning framework based on graphs and graph-based integration. Extensive experiments seven large-scale datasets link prediction node classification tasks. Heterogeneous aims learn low-dimensional vector representations of differe...