We show how feature maps in convolutional networks are susceptible to spatial
bias. Due to a combination of architectural choices, the activation at certain
locations is systematically elevated or weakened. The major source of this bias
is the padding mechanism. Depending on several aspects of convolution
arithmetic, this mechanism can apply the padding unevenly, leading to
asymmetries in the learned weights. We demonstrate how such bias can be
detrimental to certain tasks such as small object detection: the activation is
suppressed if the stimulus lies in the impacted area, leading to blind spots
and misdetection. We propose solutions to mitigate spatial bias and demonstrate
how they can improve model accuracy.

探讨卷积网络中图像特征层在空间上偏倚的问题，并提出了一些解决方案，以改善模型准确性。

注意细节 - CNNs 可能产生盲点

Mind the Pad -- CNNs can Develop Blind Spots

Inferring a cause from its effect using observed time series data is a major
challenge in natural and social sciences. Assuming the effect is generated by
the cause trough a linear system, we propose a new approach based on the
hypothesis that nature chooses the "cause" and the "mechanism that generates
the effect from the cause" independent of each other. We therefore postulate
that the power spectrum of the time series being the cause is uncorrelated with
the square of the transfer function of the linear filter generating the effect.
While most causal discovery methods for time series mainly rely on the noise,
our method relies on asymmetries of the power spectral density properties that
can be exploited even in the context of deterministic systems. We describe
mathematical assumptions in a deterministic model under which the causal
direction is identifiable with this approach. We also discuss the method's
performance under the additive noise model and its relationship to Granger
causality. Experiments show encouraging results on synthetic as well as
real-world data. Overall, this suggests that the postulate of Independence of
Cause and Mechanism is a promising principle for causal inference on empirical
time series.

本文基于 “因果独立性原则”，提出了一种新的时序数据因果推断方法，该方法并不依赖于噪声，而是从功率谱密度性质的不对称性入手，能够在确定性系统下使用。通过实验，该方法表现出了很好的效果。