574 research outputs found
On Bismut Flat Manifolds
In this paper, we give a classification of all compact Hermitian manifolds
with flat Bismut connection. We show that the torsion tensor of such a manifold
must be parallel, thus the universal cover of such a manifold is a Lie group
equipped with a bi-invariant metric and a compatible left invariant complex
structure. In particular, isosceles Hopf surfaces are the only Bismut flat
compact non-K\"ahler surfaces, while central Calabi-Eckmann threefolds are the
only simply-connected compact Bismut flat threefolds.Comment: In this 3rd version, we add a lemma on Hermitian surfaces with flat
Riemannian connection. References are updated and typos correcte
Research on the time optimization model algorithm of Customer Collaborative Product Innovation
Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented.
Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective.
Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated.
Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.Peer Reviewe
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Time series forecasting is an important and forefront task in many real-world
applications. However, most of time series forecasting techniques assume that
the training data is clean without anomalies. This assumption is unrealistic
since the collected time series data can be contaminated in practice. The
forecasting model will be inferior if it is directly trained by time series
with anomalies. Thus it is essential to develop methods to automatically learn
a robust forecasting model from the contaminated data. In this paper, we first
statistically define three types of anomalies, then theoretically and
experimentally analyze the loss robustness and sample robustness when these
anomalies exist. Based on our analyses, we propose a simple and efficient
algorithm to learn a robust forecasting model. Extensive experiments show that
our method is highly robust and outperforms all existing approaches. The code
is available at https://github.com/haochenglouis/RobustTSF.Comment: Accepted by the 12th International Conference on Learning
Representations (ICLR 2024
Research on the time optimization model algorithm of Customer Collaborative Product Innovation
Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented.
Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective.
Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated.
Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.Peer Reviewe
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Time series anomaly detection is critical for a wide range of applications.
It aims to identify deviant samples from the normal sample distribution in time
series. The most fundamental challenge for this task is to learn a
representation map that enables effective discrimination of anomalies.
Reconstruction-based methods still dominate, but the representation learning
with anomalies might hurt the performance with its large abnormal loss. On the
other hand, contrastive learning aims to find a representation that can clearly
distinguish any instance from the others, which can bring a more natural and
promising representation for time series anomaly detection. In this paper, we
propose DCdetector, a multi-scale dual attention contrastive representation
learning model. DCdetector utilizes a novel dual attention asymmetric design to
create the permutated environment and pure contrastive loss to guide the
learning process, thus learning a permutation invariant representation with
superior discrimination abilities. Extensive experiments show that DCdetector
achieves state-of-the-art results on multiple time series anomaly detection
benchmark datasets. Code is publicly available at
https://github.com/DAMO-DI-ML/KDD2023-DCdetector
Analysis of Spatial Travel Association Rules for Rail Transit Based on AFC and POI Data
In order to explore the spatial distribution rules and causes of urban rail transit passenger travel, this paper mines the spatial 1-frequent itemset and 2-frequent itemsets of weekdays and weekends metro passenger travel based on Apriori algorithm using the continuous week of Automatic Fare Collection System (AFC) swipe card. At the same time, the K-Means algorithm is used to cluster the subway stations and explore the causes of association rules by combining the Point of Interest (POI) data of the same period within the radiation range of the subway stations. The study shows that the spatial distribution pattern of inbound and outbound passenger flow of Shanghai rail transit is consistent between weekdays and weekends, and the outbound passenger flow is more concentrated than the inbound passenger flow, and the significance of weekends is higher; the spatial distribution of metro stations is "circled"; the analysis of the high-lift association rules show that a large passenger flow group centered on the type 3 station is formed in the spatial location, and the passenger flow within the group is mainly commuter flow with separation of employment and residence. The association rule mining of metro passenger travel data is beneficial to understanding the spatial distribution pattern and causes of metro ridership, which can provide reference for rail network planning and operation management
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