Generating wide-area digital surface models (DSMs) requires registering a
large number of individual, and partially overlapped DSMs. This presents a
challenging problem for a typical registration algorithm, since when a large
number of observations from these multiple DSMs are considered, it may easily
cause memory overflow. Sequential registration algorithms, although can
significantly reduce the computation, are especially vulnerable for small
overlapped pairs, leading to a large error accumulation. In this work, we
propose a novel solution that builds the DSM registration task as a motion
averaging problem: pair-wise DSMs are registered to build a scene graph, with
edges representing relative poses between DSMs. Specifically, based on the grid
structure of the large DSM, the pair-wise registration is performed using a
novel nearest neighbor search method. We show that the scene graph can be
optimized via an extremely fast motion average algorithm with O(N) complexity
(N refers to the number of images). Evaluation of high-resolution
satellite-derived DSM demonstrates significant improvement in computation and
accuracy.

通过将数字表面模型注册任务建立为一个运动平均问题，我们提出了一种新的解决方案：成对的数字表面模型通过建立场景图来注册，其中边表示数字表面模型之间的相对姿势。根据大型数字表面模型的网格结构，采用一种新的最近邻搜索方法进行成对的注册。通过极快的运动平均算法对场景图进行优化，实验证明在计算和准确性方面有显著的改进。

大规模 DSM 注册通过动作平均化

Large-scale DSM registration via motion averaging

Stereotypes inform how we present ourselves and others, and in turn how we
behave. They are thus important to measure. Recent work has used projections of
embeddings from Distributional Semantic Models (DSMs), such as BERT, to perform
these measurements. However, DSMs capture cognitive associations that are not
necessarily relevant to the interpersonal nature of stereotyping. Here, we
propose and evaluate three novel, entity-centric methods for learning
stereotypes from Twitter and Wikipedia biographies. Models are trained by
leveraging the fact that multiple phrases are applied to the same person,
magnifying the person-centric nature of the learned associations. We show that
these models outperform existing approaches to stereotype measurement with
respect to 1) predicting which identities people apply to themselves and
others, and 2) quantifying stereotypes on salient social dimensions (e.g.
gender). Via a case study, we also show the utility of these models for future
questions in computational social science.

本文提出并评估三种新的基于实体的学习方法，利用多个短语应用于同一人，从而放大所学关联的人本性质，以预测人们如何将身份应用于自己和他人，并量化社会上突出的维度上的刻板印象。实验证明这些模型在刻板印象测量方面胜过现有方法，并展示了这些模型在未来计算社会科学中的实用性。

利用实体为中心的数据测量刻板印象

Measuring Stereotypes using Entity-Centric Data

High-resolution optical satellite sensors, combined with dense stereo
algorithms, have made it possible to reconstruct 3D city models from space.
However, these models are, in practice, rather noisy and tend to miss small
geometric features that are clearly visible in the images. We argue that one
reason for the limited quality may be a too early, heuristic reduction of the
triangulated 3D point cloud to an explicit height field or surface mesh. To
make full use of the point cloud and the underlying images, we introduce
ImpliCity, a neural representation of the 3D scene as an implicit, continuous
occupancy field, driven by learned embeddings of the point cloud and a stereo
pair of ortho-photos. We show that this representation enables the extraction
of high-quality DSMs: with image resolution 0.5$\,$m, ImpliCity reaches a
median height error of $\approx\,$0.7$\,$m and outperforms competing methods,
especially w.r.t. building reconstruction, featuring intricate roof details,
smooth surfaces, and straight, regular outlines.

通过引入 ImpliCity，实现将三维场景表示为隐式连续占据场的神经表示，可以提高数字表面模型（DSM）的质量，特别是在建筑物重建方面具有明显优势。