Image inpainting has made significant advances in recent years. However, it
is still challenging to recover corrupted images with both vivid textures and
reasonable structures. Some specific methods only tackle regular textures while
losing holistic structures due to the limited receptive fields of convolutional
neural networks (CNNs). On the other hand, attention-based models can learn
better long-range dependency for the structure recovery, but they are limited
by the heavy computation for inference with large image sizes. To address these
issues, we propose to leverage an additional structure restorer to facilitate
the image inpainting incrementally. The proposed model restores holistic image
structures with a powerful attention-based transformer model in a fixed
low-resolution sketch space. Such a grayscale space is easy to be upsampled to
larger scales to convey correct structural information. Our structure restorer
can be integrated with other pretrained inpainting models efficiently with the
zero-initialized residual addition. Furthermore, a masking positional encoding
strategy is utilized to improve the performance with large irregular masks.
Extensive experiments on various datasets validate the efficacy of our model
compared with other competitors. Our codes are released in
this https URL

该论文提出了一种基于注意力机制的变形器模型依次恢复整体图像结构，利用一个附加的结构修复器，通过零初始化残差加法有效地集成预训练修补模型，采用掩膜位置编码策略提高了不规则掩膜修补效果。

增量变换器结构增强的图像修复与遮盖位置编码

Incremental Transformer Structure Enhanced Image Inpainting with Masking  Positional Encoding

Following procedural texts written in natural languages is challenging. We
must read the whole text to identify the relevant information or identify the
instruction flows to complete a task, which is prone to failures. If such texts
are structured, we can readily visualize instruction-flows, reason or infer a
particular step, or even build automated systems to help novice agents achieve
a goal. However, this structure recovery task is a challenge because of such
texts' diverse nature. This paper proposes to identify relevant information
from such texts and generate information flows between sentences. We built a
large annotated procedural text dataset (CTFW) in the cybersecurity domain
(3154 documents). This dataset contains valuable instructions regarding
software vulnerability analysis experiences. We performed extensive experiments
on CTFW with our LM-GNN model variants in multiple settings. To show the
generalizability of both this task and our method, we also experimented with
procedural texts from two other domains (Maintenance Manual and Cooking), which
are substantially different from cybersecurity. Our experiments show that Graph
Convolution Network with BERT sentence embeddings outperforms BERT in all three
domains

本研究提出了一种方法来从过程性文本中识别相关信息并在句子之间生成信息流，通过建立一个大规模的经过注释的过程性文本数据集和进行全面实验，证明了使用 LM-GNN 模型变种的图卷积网络与 BERT 句子嵌入在三个不同领域（网络安全，维护手册和烹饪）中均优于仅使用 BERT 的方法。

从程序化网络安全文本中构建流程图

Constructing Flow Graphs from Procedural Cybersecurity Texts

This paper considers the problem of learning, from samples, the dependency
structure of a system of linear stochastic differential equations, when some of
the variables are latent. In particular, we observe the time evolution of some
variables, and never observe other variables; from this, we would like to find
the dependency structure between the observed variables - separating out the
spurious interactions caused by the (marginalizing out of the) latent
variables' time series. We develop a new method, based on convex optimization,
to do so in the case when the number of latent variables is smaller than the
number of observed ones. For the case when the dependency structure between the
observed variables is sparse, we theoretically establish a high-dimensional
scaling result for structure recovery. We verify our theoretical result with
both synthetic and real data (from the stock market).

本文考虑学习基于样本的线性随机微分方程系统的依赖结构，特别是当一些变量是潜在变量时。我们开发了一种基于凸优化的新方法来解决这个问题。针对观测变量之间的依赖关系稀疏的情况，我们理论上建立了结构恢复的高维缩放结果，并通过合成和真实数据（来自股票市场）验证了我们的理论结果。