A novel wavelet-like function is presented that makes it convenient to create
filter banks given mainly two parameters that influence the focus area and the
filter count. This is accomplished by computing the inverse Fourier transform
of Gaussian functions on logarithmic frequency axes in the frequency domain.
The resulting filters are similar to Gabor filters and represent oriented brief
signal oscillations of different sizes. The wavelet-like function can be
thought of as a generalized Log-Gabor filter that is multidimensional, always
uses Gaussian functions on logarithmic frequency axes, and innately includes
low-pass filters from Gaussian functions located at the frequency domain
origin.

提出了一种新颖的类小波函数，通过在频域上的对数频率轴上计算高斯函数的傅里叶逆变换，可以方便地创建滤波器组，这些滤波器类似于 Gabor 滤波器，代表了不同尺寸的定向短小信号振荡。该类小波函数可以被看作是一个多维的广义 Log-Gabor 滤波器，始终使用在频域上的对数频率轴上的高斯函数，并从位于频域原点的高斯函数中固有地包含低通滤波器。

基于对数频率轴上的高斯函数导出的多维 Gabor-Like 滤波器

Multidimensional Gabor-Like Filters Derived from Gaussian Functions on  Logarithmic Frequency Axes

We study rough high-dimensional landscapes in which an increasingly stronger
preference for a given configuration emerges. Such energy landscapes arise in
glass physics and inference. In particular we focus on random Gaussian
functions, and on the spiked-tensor model and generalizations. We thoroughly
analyze the statistical properties of the corresponding landscapes and
characterize the associated geometrical phase transitions. In order to perform
our study, we develop a framework based on the Kac-Rice method that allows to
compute the complexity of the landscape, i.e. the logarithm of the typical
number of stationary points and their Hessian. This approach generalizes the
one used to compute rigorously the annealed complexity of mean-field glass
models. We discuss its advantages with respect to previous frameworks, in
particular the thermodynamical replica method which is shown to lead to
partially incorrect predictions.

研究高维能量景观，并使用 Kac-Rice 方法框架进行分析，以探索平均场玻璃模型的退火复杂性。

尖晶石张量和简单玻璃模型中的复杂能量景观：波折，局部极小值排列和相变

Complex energy landscapes in spiked-tensor and simple glassy models:  ruggedness, arrangements of local minima and phase transitions

Data-driven saliency has recently gained a lot of attention thanks to the use
of Convolutional Neural Networks for predicting gaze fixations. In this paper
we go beyond standard approaches to saliency prediction, in which gaze maps are
computed with a feed-forward network, and present a novel model which can
predict accurate saliency maps by incorporating neural attentive mechanisms.
The core of our solution is a Convolutional LSTM that focuses on the most
salient regions of the input image to iteratively refine the predicted saliency
map. Additionally, to tackle the center bias typical of human eye fixations,
our model can learn a set of prior maps generated with Gaussian functions. We
show, through an extensive evaluation, that the proposed architecture
outperforms the current state of the art on public saliency prediction
datasets. We further study the contribution of each key component to
demonstrate their robustness on different scenarios.

提出了一种基于卷积 LSTM 的新型模型，结合神经注意机制，可预测准确的显著图，并学习一组使用高斯函数生成的先验图，这种模型在公共显著性预测数据集上表现优于现有技术，可以克服人眼注视典型的中心偏差，并且对于不同的情境展现了关键组件各自的贡献。