Potential crowd flow prediction for new planned transportation sites is a
fundamental task for urban planners and administrators. Intuitively, the
potential crowd flow of the new coming site can be implied by exploring the
nearby sites. However, the transportation modes of nearby sites (e.g. bus
stations, bicycle stations) might be different from the target site (e.g.
subway station), which results in severe data scarcity issues. To this end, we
propose a data driven approach, named MOHER, to predict the potential crowd
flow in a certain mode for a new planned site. Specifically, we first identify
the neighbor regions of the target site by examining the geographical proximity
as well as the urban function similarity. Then, to aggregate these
heterogeneous relations, we devise a cross-mode relational GCN, a novel
relation-specific transformation model, which can learn not only the
correlations but also the differences between different transportation modes.
Afterward, we design an aggregator for inductive potential flow representation.
Finally, an LTSM module is used for sequential flow prediction. Extensive
experiments on real-world data sets demonstrate the superiority of the MOHER
framework compared with the state-of-the-art algorithms.

本文提出了一种基于数据驱动和改进传输模式的关系模型的流模型，名为 MOHER，用于预测新计划站点的潜在人流量流向及其不同的交通运输模式，并在真实数据集上进行的实验证明了 MOHER 的优越性。

为潜在人流预测建模多模式下的异质关系

Modeling Heterogeneous Relations across Multiple Modes for Potential  Crowd Flow Prediction

In this paper, we describe our method for the ISIC 2019 Skin Lesion
Classification Challenge. The challenge comes with two tasks. For task 1, skin
lesions have to be classified based on dermoscopic images. For task 2,
dermoscopic images and additional patient meta data have to be used. A diverse
dataset of 25000 images was provided for training, containing images from eight
classes. The final test set contains an additional, unknown class. We address
this challenging problem with a simple, data driven approach by including
external data with skin lesions types that are not present in the training set.
Furthermore, multi-class skin lesion classification comes with the problem of
severe class imbalance. We try to overcome this problem by using loss
balancing. Also, the dataset contains images with very different resolutions.
We take care of this property by considering different model input resolutions
and different cropping strategies. To incorporate meta data such as age,
anatomical site, and sex, we use an additional dense neural network and fuse
its features with the CNN. We aggregate all our models with an ensembling
strategy where we search for the optimal subset of models. Our best ensemble
achieves a balanced accuracy of 74.2% using five-fold cross-validation. On the
official test set our method is ranked first for both tasks with a balanced
accuracy of 63.6% for task 1 and 63.4% for task 2.

本文介绍了我们针对 ISIC 2019 皮肤病变分类挑战的方法，通过包括不在训练集中的皮肤病变类型的外部数据，使用丰富多样的数据集解决多类皮肤病变分类中的类别不平衡问题，并采用不同的分辨率和裁剪策略，以及额外的神经网络融合等技术实现了一整套模型，最终在两个任务上取得了最好的效果。