315 research outputs found
Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability
Revisiting Non-Autoregressive Translation at Scale
In real-world systems, scaling has been critical for improving the
translation quality in autoregressive translation (AT), which however has not
been well studied for non-autoregressive translation (NAT). In this work, we
bridge the gap by systematically studying the impact of scaling on NAT
behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT
models show that scaling can alleviate the commonly-cited weaknesses of NAT
models, resulting in better translation performance. To reduce the side-effect
of scaling on decoding speed, we empirically investigate the impact of NAT
encoder and decoder on the translation performance. Experimental results on the
large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger
encoder and smaller decoder) can achieve comparable performance with the
scaling model, while maintaining the superiority of decoding speed with
standard NAT models. To this end, we establish a new benchmark by validating
scaled NAT models on the scaled dataset, which can be regarded as a strong
baseline for future works. We release code and system outputs at
https://github.com/DeepLearnXMU/Scaling4NAT.Comment: 13 pages, Findings of ACL 202
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the
structure-to-sequence framework ignores the type information of the
interpretation of the code, e.g., operator, string, etc. However, introducing
the type information into the existing framework is non-trivial due to the
hierarchical dependence among the type information. In order to address the
issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for
the code comment generation task which considers the source code as an N-ary
tree with type information associated with each node. Specifically, our
framework is featured with a Type-associated Encoder and a Type-restricted
Decoder which enables adaptive summarization of the source code. We further
propose a hierarchical reinforcement learning method to resolve the training
difficulties of our proposed framework. Extensive evaluations demonstrate the
state-of-the-art performance of our framework with both the auto-evaluated
metrics and case studies.Comment: ACL 2020, Accepte
Realization of corner and helical edge states in topologically trivial band gap by twig edge
The twig edge states in graphene-like structures are viewed as the fourth
states complementary to their zigzag, bearded, and armchair counterparts. In
this work, we study a rod-in-plasma system in honeycomb lattice with twig edges
under external magnetic fields and lattice scaling and show that twig edge
states can exist in different phases of the system, such as quantum Hall phase,
quantum spin Hall phase and insulating phase. The twig edge states in the
quantum Hall phase exhibit robust one-way transmission property immune to
backscattering and thus provide a novel avenue for solving the plasma
communication blackout problem. Moreover, we demonstrate that corner and edge
states can exist within the trivial band gap of the insulating phase by
modulating the on-site potential of the twig edges. Especially, helical edge
states with the unique feature of pseudospin-momentum locking that could be
exited by chiral sources are demonstrated at the twig edges within the trivial
band gap. Our results show that many topological-like behaviors of
electromagnetic waves are not necessarily tied to the exact topology of the
systems and the twig edges and interface engineering can bring new
opportunities for more flexible manipulation of electromagnetic waves
Long-coherence pairing of low-mass conduction electrons in copper-substituted lead apatite
Two entangled qubits emerge as an essential resource for quantum control,
which are normally quantum confined with atomic precision. It seems inhibitive
that in the macroscopic scope collective qubit pairs manifest long coherence
and quantum entanglement, especially at high temperature. Here, we report this
exotic ensemble effect in solid-state sintering lead apatite samples with
copper substitution, which have been repeatedly duplicated with superior
stability and low cost. An extraordinarily low-field absorption signal of cw
electron paramagnetic resonance (EPR) spectroscopy stems from low-mass
conduction electrons implying the coherence of cuprate radicals can be
long-termly protected. The pulsed EPR experiments exhibit triplet Rabi
oscillation from paired cuprate diradicals with the coherence time exceeding 1
microsecond at 85K. We believe these appealing effects are sufficiently
promising to be applied for scalable quantum control and computation.Comment: 16 pages, 4 figure
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