675 research outputs found
On the relationship between Gaussian stochastic blockmodels and label propagation algorithms
The problem of community detection receives great attention in recent years.
Many methods have been proposed to discover communities in networks. In this
paper, we propose a Gaussian stochastic blockmodel that uses Gaussian
distributions to fit weight of edges in networks for non-overlapping community
detection. The maximum likelihood estimation of this model has the same
objective function as general label propagation with node preference. The node
preference of a specific vertex turns out to be a value proportional to the
intra-community eigenvector centrality (the corresponding entry in principal
eigenvector of the adjacency matrix of the subgraph inside that vertex's
community) under maximum likelihood estimation. Additionally, the maximum
likelihood estimation of a constrained version of our model is highly related
to another extension of label propagation algorithm, namely, the label
propagation algorithm under constraint. Experiments show that the proposed
Gaussian stochastic blockmodel performs well on various benchmark networks.Comment: 22 pages, 17 figure
イメージエンハンスメント1 ビットバンドパス ΔΣ変調器を用いた20GHz 帯 DBF 送信機に関する研究
Tohoku University博士(工学)thesi
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting
Traffic flow forecasting is challenging due to the intricate spatio-temporal
correlations in traffic flow data. Existing Transformer-based methods usually
treat traffic flow forecasting as multivariate time series (MTS) forecasting.
However, too many sensors can cause a vector with a dimension greater than 800,
which is difficult to process without information loss. In addition, these
methods design complex mechanisms to capture spatial dependencies in MTS,
resulting in slow forecasting speed. To solve the abovementioned problems, we
propose a Fast Pure Transformer Network (FPTN) in this paper. First, the
traffic flow data are divided into sequences along the sensor dimension instead
of the time dimension. Then, to adequately represent complex spatio-temporal
correlations, Three types of embeddings are proposed for projecting these
vectors into a suitable vector space. After that, to capture the complex
spatio-temporal correlations simultaneously in these vectors, we utilize
Transformer encoder and stack it with several layers. Extensive experiments are
conducted with 4 real-world datasets and 13 baselines, which demonstrate that
FPTN outperforms the state-of-the-art on two metrics. Meanwhile, the
computational time of FPTN spent is less than a quarter of other
state-of-the-art Transformer-based models spent, and the requirements for
computing resources are significantly reduced
Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion
In order to increase the prediction accuracy of the online vehicle velocity
prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused
with traffic information was presented for the multiple scenarios. Initially,
traffic scenarios were established inside the co-simulation environment. In
addition, the algorithm of a general regressive neural network (GRNN) paired
with datasets of the ego-vehicle, the front vehicle, and traffic lights was
used in traffic scenarios, which increasingly improved the prediction accuracy.
To ameliorate the robustness of the algorithm, then the strategy was optimized
by particle swarm optimization (PSO) and k-fold cross-validation to find the
optimal parameters of the neural network in real-time, which constructed a
self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to
adapt with different operating situations. The self-adaptive online PSO-GRNN
VVP strategy was then deployed to a variety of simulated scenarios to test its
efficacy under various operating situations. Finally, the simulation results
reveal that in urban and highway scenarios, the prediction accuracy is
separately increased by 27.8% and 54.5% when compared to the traditional GRNN
VVP strategy with fixed parameters utilizing only the historical ego-vehicle
velocity dataset.Comment: 9 pages, 7 figure
Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud
This paper investigates the indistinguishable points (difficult to predict
label) in semantic segmentation for large-scale 3D point clouds. The
indistinguishable points consist of those located in complex boundary, points
with similar local textures but different categories, and points in isolate
small hard areas, which largely harm the performance of 3D semantic
segmentation. To address this challenge, we propose a novel Indistinguishable
Area Focalization Network (IAF-Net), which selects indistinguishable points
adaptively by utilizing the hierarchical semantic features and enhances
fine-grained features for points especially those indistinguishable points. We
also introduce multi-stage loss to improve the feature representation in a
progressive way. Moreover, in order to analyze the segmentation performances of
indistinguishable areas, we propose a new evaluation metric called
Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the
comparable results with state-of-the-art performance on several popular 3D
point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other
methods on IPBM.Comment: AAAI202
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