Mobile traffic data in urban regions shows differentiated patterns during
different hours of the day. The exploitation of these patterns enables highly
accurate mobile traffic prediction for proactive network management. However,
recent Deep Learning (DL) driven studies have only exploited spatiotemporal
features and have ignored the geographical correlations, causing high
complexity and erroneous mobile traffic predictions. This paper addresses these
limitations by proposing an enhanced mobile traffic prediction scheme that
combines the clustering strategy of daily mobile traffic peak time and novel
multi Temporal Convolutional Network with a Long Short Term Memory (multi
TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the
same hour of the day are clustered together. Our experiments on large-scale
real-world mobile traffic data show up to 28% performance improvement compared
to state-of-the-art studies, which confirms the efficacy and viability of the
proposed approach.

提出了一种增强型移动流量预测方案，该方案结合了每日移动流量高峰时间的聚类策略和新颖的多时间卷积网络与长短期记忆模型 (multi TCN-LSTM)，实验结果显示与最先进技术相比，性能提升达到 28％，证实了该方法的功效和可行性。

基于区域相关性的时空深度学习辅助移动流量预测

Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal  Deep Learning

Panoptic segmentation is the combination of semantic and instance
segmentation: assign the points in a 3D point cloud to semantic categories and
partition them into distinct object instances. It has many obvious applications
for outdoor scene understanding, from city mapping to forest management.
Existing methods struggle to segment nearby instances of the same semantic
category, like adjacent pieces of street furniture or neighbouring trees, which
limits their usability for inventory- or management-type applications that rely
on object instances. This study explores the steps of the panoptic segmentation
pipeline concerned with clustering points into object instances, with the goal
to alleviate that bottleneck. We find that a carefully designed clustering
strategy, which leverages multiple types of learned point embeddings,
significantly improves instance segmentation. Experiments on the NPM3D urban
mobile mapping dataset and the FOR-instance forest dataset demonstrate the
effectiveness and versatility of the proposed strategy.

研究通过聚类策略和点嵌入来改进全景分割的实例分割步骤，以解决同一个语义类别附近实例分割的挑战，实验证明该策略的有效性和多样性。