In this paper, we propose a multi-scale deep feature learning method for
high-resolution satellite image classification. Specifically, we firstly warp
the original satellite image into multiple different scales. The images in each
scale are employed to train a deep convolutional neural network (DCNN).
However, simultaneously training multiple DCNNs is time-consuming. To address
this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since
different SPP-nets have the same number of parameters, which share the
identical initial values, and only fine-tuning the parameters in
fully-connected layers ensures the effectiveness of each network, thereby
greatly accelerating the training process. Then, the multi-scale satellite
images are fed into their corresponding SPP-nets respectively to extract
multi-scale deep features. Finally, a multiple kernel learning method is
developed to automatically learn the optimal combination of such features.
Experiments on two difficult datasets show that the proposed method achieves
favorable performance compared to other state-of-the-art methods.

本文提出了一种用于高分辨率卫星图像分类的多尺度深度特征学习方法，该方法使用卷积神经网络（CNN）和空间金字塔池（SPP-net）技术提高了训练效率，并开发了一种多核学习方法以自动学习这些特征的最佳组合，实验表明该方法相对于其他最先进的方法具有更好的性能。

学习高分辨率卫星图像分类的多尺度深度特征

Learning Multi-Scale Deep Features for High-Resolution Satellite Image  Classification

Face detection is challenging as faces in images could be present at
arbitrary locations and in different scales. We propose a three-stage cascade
structure based on fully convolutional neural networks (FCNs). It first
proposes the approximate locations where the faces may be, then aims to find
the accurate location by zooming on to the faces. Each level of the FCN cascade
is a multi-scale fully-convolutional network, which generates scores at
different locations and in different scales. A score map is generated after
each FCN stage. Probable regions of face are selected and fed to the next
stage. The number of proposals is decreased after each level, and the areas of
regions are decreased to more precisely fit the face. Compared to passing
proposals directly between stages, passing probable regions can decrease the
number of proposals and reduce the cases where first stage doesn't propose good
bounding boxes. We show that by using FCN and score map, the FCN cascade face
detector can achieve strong performance on public datasets.

本研究提出一种基于全卷积神经网络的三级级联结构，采用多尺度全卷积网络生成得分图，使用可能区域而非建议直接连接下一级级联，通过使用得分图可以在公共数据集上获得强大的人脸检测性能。

多尺度级联全卷积网络人脸检测器

A Multi-Scale Cascade Fully Convolutional Network Face Detector

The network architecture of the human brain has become a feature of
increasing interest to the neuroscientific community, largely because of its
potential to illuminate human cognition, its variation over development and
aging, and its alteration in disease or injury. Traditional tools and
approaches to study this architecture have largely focused on single scales --
of topology, time, and space. Expanding beyond this narrow view, we focus this
review on pertinent questions and novel methodological advances for the
multi-scale brain. We separate our exposition into content related to
multi-scale topological structure, multi-scale temporal structure, and
multi-scale spatial structure. In each case, we recount empirical evidence for
such structures, survey network-based methodological approaches to reveal these
structures, and outline current frontiers and open questions. Although
predominantly peppered with examples from human neuroimaging, we hope that this
account will offer an accessible guide to any neuroscientist aiming to measure,
characterize, and understand the full richness of the brain's multiscale
network structure -- irrespective of species, imaging modality, or spatial
resolution.

本文介绍了多尺度脑网络结构的内容、方法和现状，并提供了实证证据、网络方法和当前领域的前沿和未解决问题。

多尺度脑网络

Multi-scale brain networks

Most methods proposed to uncover communities in complex networks rely on
combinatorial graph properties. Usually an edge-counting quality function, such
as modularity, is optimized over all partitions of the graph compared against a
null random graph model. Here we introduce a systematic dynamical framework to
design and analyze a wide variety of quality functions for community detection.
The quality of a partition is measured by its Markov Stability, a
time-parametrized function defined in terms of the statistical properties of a
Markov process taking place on the graph. The Markov process provides a
dynamical sweeping across all scales in the graph, and the time scale is an
intrinsic parameter that uncovers communities at different resolutions.
This dynamic-based community detection leads to a compound optimization,
which favours communities of comparable centrality (as defined by the
stationary distribution), and provides a unifying framework for spectral
algorithms, as well as different heuristics for community detection, including
versions of modularity and Potts model. Our dynamic framework creates a
systematic link between different stochastic dynamics and their corresponding
notions of optimal communities under distinct (node and edge) centralities. We
show that the Markov Stability can be computed efficiently to find multi-scale
community structure in large networks.

该论文提出了一种基于 Markov Stability 的动态社区检测框架，该框架可以通过时间参数化函数自动查找在不同分辨率下的社区结构，并且能够高效地在大型网络中应用。