Network embedding is an effective technique to learn the low-dimensional
representations of nodes in networks. Real-world networks are usually with
multiplex or having multi-view representations from different relations.
Recently, there has been increasing interest in network embedding on multiplex
data. However, most existing multiplex approaches assume that the data is
complete in all views. But in real applications, it is often the case that each
view suffers from the missing of some data and therefore results in partial
multiplex data. In this paper, we present a novel Deep Partial Multiplex
Network Embedding approach to deal with incomplete data. In particular, the
network embeddings are learned by simultaneously minimizing the deep
reconstruction loss with the autoencoder neural network, enforcing the data
consistency across views via common latent subspace learning, and preserving
the data topological structure within the same network through graph Laplacian.
We further prove the orthogonal invariant property of the learned embeddings
and connect our approach with the binary embedding techniques. Experiments on
four multiplex benchmarks demonstrate the superior performance of the proposed
approach over several state-of-the-art methods on node classification, link
prediction and clustering tasks.

本文提出了一种新颖的深度部分多重网络嵌入方法，利用自编码器神经网络，共同的潜在子空间学习和图 Laplacian 的方法学习网络嵌入，以处理不完整的多重数据，并在节点分类，链接预测和聚类任务中展示了卓越的性能。

深度部分多重网络嵌入

Deep Partial Multiplex Network Embedding

State estimation is of considerable significance for the power system
operation and control. However, well-designed false data injection attacks can
utilize blind spots in conventional residual-based bad data detection methods
to manipulate measurements in a coordinated manner and thus affect the secure
operation and economic dispatch of grids. In this paper, we propose a detection
approach based on an autoencoder neural network. By training the network on the
dependencies intrinsic in 'normal' operation data, it effectively overcomes the
challenge of unbalanced training data that is inherent in power system attack
detection. To evaluate the detection performance of the proposed mechanism, we
conduct a series of experiments on the IEEE 118-bus power system. The
experiments demonstrate that the proposed autoencoder detector displays robust
detection performance under a variety of attack scenarios.

本文提出了一种基于自动编码器神经网络的检测方法，通过训练数据依赖性有效地克服了电力系统攻击检测中固有的训练数据不平衡性，实现了对假数据注入攻击的检测，并在 IEEE 118 节点电力系统上进行了实验验证性能表现良好。

利用自编码器方法检测虚假数据注入攻击

Detection of False Data Injection Attacks Using the Autoencoder Approach

Concise, accurate descriptions of physical systems through their conserved
quantities abound in the natural sciences. In data science, however, current
research often focuses on regression problems, without routinely incorporating
additional assumptions about the system that generated the data. Here, we
propose to explore a particular type of underlying structure in the data:
Hamiltonian systems, where an "energy" is conserved. Given a collection of
observations of such a Hamiltonian system over time, we extract phase space
coordinates and a Hamiltonian function of them that acts as the generator of
the system dynamics. The approach employs an autoencoder neural network
component to estimate the transformation from observations to the phase space
of a Hamiltonian system. An additional neural network component is used to
approximate the Hamiltonian function on this constructed space, and the two
components are trained jointly. As an alternative approach, we also demonstrate
the use of Gaussian processes for the estimation of such a Hamiltonian. After
two illustrative examples, we extract an underlying phase space as well as the
generating Hamiltonian from a collection of movies of a pendulum. The approach
is fully data-driven, and does not assume a particular form of the Hamiltonian
function.

本文提出一种全数据驱动的方法，利用自编码神经网络组件估计哈密顿系统的相空间，通过另一个神经网络组件来逼近其哈密顿函数并在两个组件之间进行联合训练，提取了摆锤的相空间和生成哈密顿函数。

从数据中学习哈密顿系统

On Learning Hamiltonian Systems from Data

When using ultrasound video as input, Deep Neural Network-based Silent Speech
Interfaces usually rely on the whole image to estimate the spectral parameters
required for the speech synthesis step. Although this approach is quite
straightforward, and it permits the synthesis of understandable speech, it has
several disadvantages as well. Besides the inability to capture the relations
between close regions (i.e. pixels) of the image, this pixel-by-pixel
representation of the image is also quite uneconomical. It is easy to see that
a significant part of the image is irrelevant for the spectral parameter
estimation task as the information stored by the neighbouring pixels is
redundant, and the neural network is quite large due to the large number of
input features. To resolve these issues, in this study we train an autoencoder
neural network on the ultrasound image; the estimation of the spectral speech
parameters is done by a second DNN, using the activations of the bottleneck
layer of the autoencoder network as features. In our experiments, the proposed
method proved to be more efficient than the standard approach: the measured
normalized mean squared error scores were lower, while the correlation values
were higher in each case. Based on the result of a listening test, the
synthesized utterances also sounded more natural to native speakers. A further
advantage of our proposed approach is that, due to the (relatively) small size
of the bottleneck layer, we can utilize several consecutive ultrasound images
during estimation without a significant increase in the network size, while
significantly increasing the accuracy of parameter estimation.

通过对超声图像训练自编码神经网络，将神经网络的输入特征从像素级别转移为自编码器的瓶颈层激活，以更高的效率和准确性估计语音合成所需的频谱参数。