188 research outputs found
Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
How to handle time features shall be the core question of any time series
forecasting model. Ironically, it is often ignored or misunderstood by
deep-learning based models, even those baselines which are state-of-the-art.
This behavior makes their inefficient, untenable and unstable. In this paper,
we rigorously analyze three prevalent but deficient/unfounded deep time series
forecasting mechanisms or methods from the view of time series properties,
including normalization methods, multivariate forecasting and input sequence
length. Corresponding corollaries and solutions are given on both empirical and
theoretical basis. We thereby propose a novel time series forecasting network,
i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be
combined with both supervised and self-supervised forecasting format. Thanks to
the core idea of respecting time series properties, no matter in which
forecasting format, RTNet shows obviously superior forecasting performances
compared with dozens of other SOTA time series forecasting baselines in three
real-world benchmark datasets. By and large, it even occupies less time
complexity and memory usage while acquiring better forecasting accuracy. The
source code is available at https://github.com/OrigamiSL/RTNet
China's Role in the Rising of the South: Vision for 2030
This article examines China's major development contributions, looking at its wider impact on world development. In particular, the article examines the impact of China's development on the changing pattern between the North and South and the human development index. The factors and related regimes behind these phenomena are discussed and a conceptual model is constructed, providing a meta?analysis of the evolution of China's role, based on the structural interpretation of external impetus and barriers, as well as internal advantages and shortcomings. The authors' long?term projections show that the rise of the South, led by China, will be the most important shift in the world's landscape with respect to the development of the emerging world, perhaps leading other large developing economies to play a more prominent role in international development in the future, bringing common development, common prosperity and common progress to the world
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
With the recent advances in mobile energy storage technologies, electric
vehicles (EVs) have become a crucial part of smart grids. When EVs participate
in the demand response program, the charging cost can be significantly reduced
by taking full advantage of the real-time pricing signals. However, many
stochastic factors exist in the dynamic environment, bringing significant
challenges to design an optimal charging/discharging control strategy. This
paper develops an optimal EV charging/discharging control strategy for
different EV users under dynamic environments to maximize EV users' benefits.
We first formulate this problem as a Markov decision process (MDP). Then we
consider EV users with different behaviors as agents in different environments.
Furthermore, a horizontal federated reinforcement learning (HFRL)-based method
is proposed to fit various users' behaviors and dynamic environments. This
approach can learn an optimal charging/discharging control strategy without
sharing users' profiles. Simulation results illustrate that the proposed
real-time EV charging/discharging control strategy can perform well among
various stochastic factors
Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP
The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
This paper targets at improving the generalizability of hypergraph neural
networks in the low-label regime, through applying the contrastive learning
approach from images/graphs (we refer to it as HyperGCL). We focus on the
following question: How to construct contrastive views for hypergraphs via
augmentations? We provide the solutions in two folds. First, guided by domain
knowledge, we fabricate two schemes to augment hyperedges with higher-order
relations encoded, and adopt three vertex augmentation strategies from
graph-structured data. Second, in search of more effective views in a
data-driven manner, we for the first time propose a hypergraph generative model
to generate augmented views, and then an end-to-end differentiable pipeline to
jointly learn hypergraph augmentations and model parameters. Our technical
innovations are reflected in designing both fabricated and generative
augmentations of hypergraphs. The experimental findings include: (i) Among
fabricated augmentations in HyperGCL, augmenting hyperedges provides the most
numerical gains, implying that higher-order information in structures is
usually more downstream-relevant; (ii) Generative augmentations do better in
preserving higher-order information to further benefit generalizability; (iii)
HyperGCL also boosts robustness and fairness in hypergraph representation
learning. Codes are released at https://github.com/weitianxin/HyperGCL.Comment: NeurIPS 2022. Supplementary materials are available at
https://weitianxin.github.io/files/neurips22_hypergcl_appendix.pd
- …