Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE model itself suffers from deficiencies such as poor manifold recovery when data lie on low-dimensional manifold embedded in higher dimensional ambient space and they manifest themselves in each applications differently. The consequences of it in drug discovery is somewhat under-explored. In this paper, we study how to improve the similarity of the data generated via VAE and the training dataset by improving manifold recovery via a 2-stage VAE where the second stage VAE is trained on the latent space of the first one. We experimentally evaluated our approach using the ChEMBL dataset as well as a polymer datasets. In both dataset, the 2-stage VAE method is able to improve the property statistics significantly from a pre-existing method.

变形AutoEncoders（VAES）是最常用的生成模型之一，特别是对于图像数据。训练VAE中的突出困难是在低维歧管上支持的数据。戴伊和WIPF（2019年）的最新工作表明，在低维数据上，发电机将收敛到具有0方差的解决方案，该方案被正确地支持地面真相歧管。在本文中，通过组合理论和经验结果，我们表明故事更加微妙。正是，我们表明，对于线性编码器/解码器，故事大多是真实的，VAE训练确实恢复了一个等于地面真理歧管的支撑的发电机，但这是由于梯度下降的隐含偏差而不是仅仅是vae损失本身。在非线性案例中，我们表明VAE训练经常学习更高度的歧管，这是地面真相歧管的超集。

随着深度学习（DL）的发展，自然语言处理（NLP）使我们可以分析和理解大量语言文本。因此，在NLP的帮助下，我们可以在联合语义源和噪声频道上进行联合语义源和信道进行语义通信。然而，实现这一目标的现有方法是使用NLP的固定变压器，同时忽略每个句子中包含的语义信息的差异。为了解决这个问题，我们提出了一种基于通用变压器的新语义通信系统。与传统变压器相比，在通用变压器中引入了自适应循环机制。通过引入循环机制，新的语义通信系统可以更灵活地传输具有不同语义信息的句子，并在各种信道条件下实现更好的端到端性能。

用皮肤镜图像进行深度学习的黑色素瘤分类最近显示出在自动早期黑色素瘤诊断中的巨大潜力。然而，受到明显的数据失衡和明显的外部伪影的限制，即头发和尺子标记，从皮肤镜图像中提取的判别特征提取非常具有挑战性。在这项研究中，我们试图分别解决这些问题，以更好地表示病变特征。具体而言，基于GAN的数据增强（GDA）策略可与拟议的隐式脱糖（IHD）策略一起生成合成黑色素瘤阳性图像。其中，与头发相关的表示通过辅助分类器网络隐式分散，并反向发送到黑色素瘤 - 特征提取主链，以提供更好的黑色素瘤特异性表示学习。此外，为了训练IHD模块，头发的噪音还标记在ISIC2020数据集上，这使其成为第一个带有类似头发伪影的注释的大型皮肤镜数据集。广泛的实验证明了所提出的框架的优势以及每个组件的有效性。改进的数据集可在https://github.com/kirtsy/dermoscopicdataset上公开可用。

最近，深度神经网络具有极大的高级无效磁共振图像（MRI）重建，其中大多数研究都遵循单个解剖学中的一个网络时尚，即每个专家网络都经过训练和评估特定解剖结构。除了培训多个独立模型的效率低下之外，此类公约还忽略了各种解剖学的共享脱张知识，这些知识可以彼此受益。为了探索共享知识，一种天真的方法是将来自各种解剖学的所有数据结合起来，以训练全能网络。不幸的是，尽管存在共同的脱氧知识，但我们透露，不同解剖学的独家知识可能会恶化特定的重建目标，从而导致整体绩效降低。在这项研究中观察到这一点，我们提出了一个新型的深MRI重建框架，并具有解剖结构和解剖学特异性的参数化学习者，旨在“寻求共同点，同时解决不同的解剖学差异”。尤其是主要的解剖学共享的学习者是暴露于不同的解剖学上，以模拟蓬勃发展的共同知识，而有效的解剖学特异性学习者则接受了目标解剖结构的培训，以进行独家知识。在两个MRI重建网络中，在我们的框架顶部介绍并探索了四个不同的解剖学学习者实现。关于大脑，膝盖和心脏MRI数据集的全面实验表明，其中三个学习者能够通过多种解剖学协作学习来增强重建性能。

联合学习（FL）已出现联合列车在IOT中具有分布式数据集的模型，同时避免对中央数据收集的需求。由于观察范围有限，这种数据集只能反映当地信息，这限制了训练型的型号的质量。在实际网络中，全球信息和本地观察总是共存，这需要联合考虑学习做出合理的政策。然而，在分布式客户端中的水平流域中，中央代理机构仅作为模型聚合器，而不利用其全局功能进一步改进模型。这可能在很大程度上降低了一些任务中的性能，例如流量预测，其中全球信息明显提高了准确性。同时，这种全局特征可能不会直接发送给用于数据安全的代理。然后如何利用居住在中央机构的全球观察，同时保护其安全升起作为FL中的重要问题。在本文中，我们开发了垂直横向联合学习（VHFL）过程，其中全局特征在没有额外通信轮的过程中与代理共享代理。考虑到延迟和数据包丢失，我们分析了网络系统的收敛性并通过实验验证了其性能。建议的VHFL可以提高与水平FL相比的准确性，同时保护全球数据的安全性。

In this work we study statistical properties of graph-based algorithms for multi-manifold clustering (MMC). In MMC the goal is to retrieve the multi-manifold structure underlying a given Euclidean data set when this one is assumed to be obtained by sampling a distribution on a union of manifolds $\mathcal{M} = \mathcal{M}_1 \cup\dots \cup \mathcal{M}_N$ that may intersect with each other and that may have different dimensions. We investigate sufficient conditions that similarity graphs on data sets must satisfy in order for their corresponding graph Laplacians to capture the right geometric information to solve the MMC problem. Precisely, we provide high probability error bounds for the spectral approximation of a tensorized Laplacian on $\mathcal{M}$ with a suitable graph Laplacian built from the observations; the recovered tensorized Laplacian contains all geometric information of all the individual underlying manifolds. We provide an example of a family of similarity graphs, which we call annular proximity graphs with angle constraints, satisfying these sufficient conditions. We contrast our family of graphs with other constructions in the literature based on the alignment of tangent planes. Extensive numerical experiments expand the insights that our theory provides on the MMC problem.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.