Blasted rock material serves a critical role in various engineering
applications, yet the phenomenon of segregation-where particle sizes vary
significantly along the gradient of a quarry pile-presents challenges for
optimizing quarry material storage and handling. This study introduces an
advanced image analysis methodology to characterize such segregation of rock
fragments. The accurate delineation of detailed rock fragment size
distributions was achieved through the analysis of drone-captured imagery,
coupled with the application of an enhanced Unet semantic segmentation model
integrated with an expansion-based post-processing technique. The quarry slope
was stratified into four vertical sections, with the size distribution of each
section quantified via ellipsoid shape approximations. Our results disclose
pronounced vertical segregation patterns, with finer particles concentrated in
the upper slope regions and coarser particles in the lower. Utilizing relative
characteristic diameters, we offered insight into the degree of segregation,
thereby illustrating the spatial heterogeneity in fragment size more clearly.
The techniques outlined in this study deliver a scalable and accurate method
for assessing fragment size distribution, with the potential to better inform
resource management and operational decisions in quarry management.

应用先进的图像分析方法，采用改进的 Unet 语义分割模型和基于扩展的后处理技术，成功地刻画了爆破岩石颗粒的细分离现象，并通过相对特征直径提供了岩石颗粒尺寸的空间异质性，为采石场管理中资源管理和运营决策提供了更好的信息。

通过利用航拍图像分析实现对爆破堆石中分离性的表征

Characterizing segregation in blast rock piles a deep-learning approach  leveraging aerial image analysis

This work aims to explore the community structure of Santiago de Chile by
analyzing the movement patterns of its residents. We use a dataset containing
the approximate locations of home and work places for a subset of anonymized
residents to construct a network that represents the movement patterns within
the city. Through the analysis of this network, we aim to identify the
communities or sub-cities that exist within Santiago de Chile and gain insights
into the factors that drive the spatial organization of the city. We employ
modularity optimization algorithms and clustering techniques to identify the
communities within the network. Our results present that the novelty of
combining community detection algorithms with segregation tools provides new
insights to further the understanding of the complex geography of segregation
during working hours.

本文旨在通过分析居民的移动模式，探索智利圣地亚哥的社区结构。我们使用一个包含匿名居民的家庭和工作地点的近似位置数据集来构建一个代表城市内移动模式的网络。通过对该网络的分析，我们旨在识别智利圣地亚哥内存在的社区或子城市，并深入了解驱动城市空间组织的因素。我们采用模块化优化算法和聚类技术来识别网络中的社区。我们的结果表明，将社区检测算法与分离工具相结合，提供了对工作时间内复杂分离地理的新认识。

利用网络度量探索底层移动模式的社群结构

Using network metrics to explore the community structure that underlies  movement patterns

Controversies around race and machine learning have sparked debate among
computer scientists over how to design machine learning systems that guarantee
fairness. These debates rarely engage with how racial identity is embedded in
our social experience, making for sociological and psychological complexity.
This complexity challenges the paradigm of considering fairness to be a formal
property of supervised learning with respect to protected personal attributes.
Racial identity is not simply a personal subjective quality. For people labeled
"Black" it is an ascribed political category that has consequences for social
differentiation embedded in systemic patterns of social inequality achieved
through both social and spatial segregation. In the United States, racial
classification can best be understood as a system of inherently unequal status
categories that places whites as the most privileged category while signifying
the Negro/black category as stigmatized. Social stigma is reinforced through
the unequal distribution of societal rewards and goods along racial lines that
is reinforced by state, corporate, and civic institutions and practices. This
creates a dilemma for society and designers: be blind to racial group
disparities and thereby reify racialized social inequality by no longer
measuring systemic inequality, or be conscious of racial categories in a way
that itself reifies race. We propose a third option. By preceding group
fairness interventions with unsupervised learning to dynamically detect
patterns of segregation, machine learning systems can mitigate the root cause
of social disparities, social segregation and stratification, without further
anchoring status categories of disadvantage.

通过在群体公平干预之前采用无监督学习来动态检测隔离模式，机器学习系统可以减轻社会差距，社会分离和分层的根源，而不进一步锚定劣势状态类别。

机器学习中的种族分类

Racial categories in machine learning

This paper introduces and analyses some models in the framework of Mean Field
Games describing interactions between two populations motivated by the studies
on urban settlements and residential choice by Thomas Schelling. For static
games, a large population limit is proved. For differential games with noise,
the existence of solutions is established for the systems of partial
differential equations of Mean Field Game theory, in the stationary and in the
evolutive case. Numerical methods are proposed, with several simulations. In
the examples and in the numerical results, particular emphasis is put on the
phenomenon of segregation between the populations.

本文介绍和分析了平均场博弈框架下描述两个人群相互作用的一些模型，探讨了静态和带噪声微分游戏的解的存在性，提出了数值方法和模拟，强调了人口之间种族隔离现象。