In this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading.
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?
In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading.
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy.
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
A liquid scintillator (LS) is developed for the Taishan Antineutrino Observatory (TAO), a ton-level neutrino detector to measure the reactor antineutrino spectrum with sub-percent energy resolution by adopting Silicon Photomultipliers (SiPMs) as photosensor.
Instrumentation and Detectors High Energy Physics - Experiment
In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions.
This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation.
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.