318 research outputs found
Association between metabolic abnormalities and HBV related hepatocelluar carcinoma in Chinese: A cross-sectional study
Low-Dimensional Inorganic Nanofunctional Materials: Design, Assembly, and Application for Chemical Engineering (I)
Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation
© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions.Peer reviewe
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Unsupervised person re-identification has achieved great success through the
self-improvement of individual neural networks. However, limited by the lack of
diversity of discriminant information, a single network has difficulty learning
sufficient discrimination ability by itself under unsupervised conditions. To
address this limit, we develop a population-based evolutionary gaming (PEG)
framework in which a population of diverse neural networks is trained
concurrently through selection, reproduction, mutation, and population mutual
learning iteratively. Specifically, the selection of networks to preserve is
modeled as a cooperative game and solved by the best-response dynamics, then
the reproduction and mutation are implemented by cloning and fluctuating
hyper-parameters of networks to learn more diversity, and population mutual
learning improves the discrimination of networks by knowledge distillation from
each other within the population. In addition, we propose a cross-reference
scatter (CRS) to approximately evaluate re-ID models without labeled samples
and adopt it as the criterion of network selection in PEG. CRS measures a
model's performance by indirectly estimating the accuracy of its predicted
pseudo-labels according to the cohesion and separation of the feature space.
Extensive experiments demonstrate that (1) CRS approximately measures the
performance of models without labeled samples; (2) and PEG produces new
state-of-the-art accuracy for person re-identification, indicating the great
potential of population-based network cooperative training for unsupervised
learning.Comment: Accepted in IJC
Unprotected quadratic band crossing points and quantum anomalous Hall effect in FeB2 monolayer
Quadratic band crossing points (QBCPs) and quantum anomalous Hall effect
(QAHE) have attracted the attention of both theoretical and experimental
researchers in recent years. Based on first-principle calculations, we find
that the FeB monolayer is a nonmagnetic semimetal with QBCPs at .
Through symmetry analysis and invariant theory, we
find that the QBCP is not protected by rotation symmetry and consists of two
Dirac points with same chirality (Berry phase of ). Once introducing
Coulomb interactions, we find that there is a
spontaneous-time-reversal-breaking instability of the spinful QBCPs, which
gives rise to a QAH insulator with orbital moment ordering
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