Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.

精确分割是分析心脏周期语义信息并使用心血管信号捕获异常的至关重要的第一步。但是，在深层语义分割领域，通常会单方面与数据的个体属性相混淆。走向心血管信号，准周期性是要学习的必不可少的特征，被视为形态学属性（AM）和节奏（AR）的合成。我们的关键见解是在深度表示的生成过程中抑制对AM或AR的过度依赖性。为了解决这个问题，我们建立了一个结构性因果模型，作为分别自定义AM和AR的干预方法的基础。在本文中，我们提出了对比性因果干预（CCI），以在框架级对比框架下形成一种新颖的训练范式。干预可以消除单个属性带来的隐式统计偏见，并导致更客观的表示。我们对QRS位置和心脏声音分割的受控条件进行了全面的实验。最终结果表明，我们的方法显然可以将QRS位置的性能提高高达0.41％，心脏声音分段为2.73％。该方法的效率推广到多个数据库和嘈杂的信号。

从单个下雨的图像中取出雨ste的人是一项挑战，因为雨牛排在多雨的图像上在空间上有所不同。本文通过结合常规图像处理技术和深度学习技术来研究此问题。提出了改进的加权引导图像过滤器（IWGIF），以从多雨图像中提取高频信息。高频信息主要包括雨牛排和噪音，它可以指导雨牛排意识到深度卷积神经网络（RSADCNN），以更多地注意雨牛排。RSADNN的效率和解释能力得到了提高。实验表明，就定性和定量测量而言，所提出的算法在合成和现实世界图像上都显着优于合成和现实世界图像的最先进方法。它对于在雨季中的自主导航很有用。

针对现有的单一图像雾度去除算法，其基于现有知识和假设，受到实际应用中的许多限制，并且可能遭受噪声和光晕放大。本文提出了端到端系统，以通过结合先前的知识和深度学习方法来减少缺陷。雾度图像首先通过加权引导图像滤波器（WGIF）分解到基础层和细节层中，并且从基层估计偶极。然后，基础层图像被传递到高效的深卷积网络，用于估计传输映射。为了在不放大天空或严重朦胧场景中完全放大噪声的情况下恢复接近相机的物体，基于传输映射的值提出自适应策略。如果像素的传输映射很小，则最终使用雾度图像的基层通过大气散射模型恢复无雾图像。否则，使用雾霾图像。实验表明，该方法对现有方法实现了卓越的性能。

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/ msracver/Deformable-ConvNets.

Vertical federated learning is a trending solution for multi-party collaboration in training machine learning models. Industrial frameworks adopt secure multi-party computation methods such as homomorphic encryption to guarantee data security and privacy. However, a line of work has revealed that there are still leakage risks in VFL. The leakage is caused by the correlation between the intermediate representations and the raw data. Due to the powerful approximation ability of deep neural networks, an adversary can capture the correlation precisely and reconstruct the data. To deal with the threat of the data reconstruction attack, we propose a hashing-based VFL framework, called \textit{HashVFL}, to cut off the reversibility directly. The one-way nature of hashing allows our framework to block all attempts to recover data from hash codes. However, integrating hashing also brings some challenges, e.g., the loss of information. This paper proposes and addresses three challenges to integrating hashing: learnability, bit balance, and consistency. Experimental results demonstrate \textit{HashVFL}'s efficiency in keeping the main task's performance and defending against data reconstruction attacks. Furthermore, we also analyze its potential value in detecting abnormal inputs. In addition, we conduct extensive experiments to prove \textit{HashVFL}'s generalization in various settings. In summary, \textit{HashVFL} provides a new perspective on protecting multi-party's data security and privacy in VFL. We hope our study can attract more researchers to expand the application domains of \textit{HashVFL}.

Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1) It is crucial for subgraphs to learn positional features to acquire structural information in the base graph in which they exist. Although the existing subgraph neural network method is capable of learning disentangled position encodings, the overall computational complexity is very high. 2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure. Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only a small number of nodes in the base graph are contained in subgraphs, which leads to a potential ``bias'' problem that the subgraph representation learning is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to be fully learned, which reduces the generalization ability of subgraph representation learning. In this paper, we aim to address the challenges above and propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL. Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder, and we propose exploratory and exploitable views for subgraph contrastive learning. Extensive experiment results on three real-world datasets show the superiority of our proposed method over state-of-the-art baselines.

通过自我监督的学习预先训练的大型语言模型在各种各样的任务上表现出令人印象深刻的零击功能。在这项工作中，我们介绍了Welm：一种针对中文的精心读取的预训练的语言模型，能够无缝执行不同类型的任务，以零或几次演示。 Welm通过“阅读”涵盖广泛主题的精选高质量语料库来接受10b参数的培训。我们表明，韦尔姆拥有有关各种领域和语言的广泛知识。在18个单语（中文）任务中，WELM可以大大优于现有的预训练模型，尺寸相似，并匹配高达25倍大的模型的性能。韦尔姆还表现出强大的多种语言和代码转换理解的能力，优于预先对30种语言进行预培训的现有多语言模型。此外，我们收集了人工编写的提示，并通过多次培训进行了大量的中文和微调韦尔姆的监督数据集。最终的模型可以实现对看不见的任务类型的强烈概括，并在零射门学习中优于无监督的韦尔姆。最后，我们证明韦尔姆具有解释和校准自己的决策的基本技能，这可能是未来研究的有希望的方向。我们的模型可以从https://welm.weixin.qq.com/docs/api/应用。

了解神经网络的决策过程很难。解释的一种重要方法是将其决定归因于关键特征。尽管提出了许多算法，但其中大多数仅改善了模型的忠诚。但是，真实的环境包含许多随机噪声，这可能会导致解释中的波动。更严重的是，最近的作品表明，解释算法容易受到对抗性攻击的影响。所有这些使解释很难在实际情况下信任。为了弥合这一差距，我们提出了一种模型 - 不稳定方法\ emph {特征归因}（METFA）的中位数测试，以量化不确定性并提高使用理论保证的解释算法的稳定性。 METFA具有以下两个函数：（1）检查一个特征是显着重要还是不重要，并生成METFA相关的映射以可视化结果； （2）计算特征归因评分的置信区间，并生成一个平滑的图表以提高解释的稳定性。实验表明，METFA提高了解释的视觉质量，并在保持忠诚的同时大大减少了不稳定。为了定量评估不同噪音设置下解释的忠诚，我们进一步提出了几个强大的忠诚指标。实验结果表明，METFA平滑的解释可以显着提高稳健的忠诚。此外，我们使用两种方案来显示METFA在应用程序中的潜力。首先，当应用于SOTA解释方法来定位语义分割模型的上下文偏见时，METFA很重要的解释使用较小的区域来维持99 \％+忠实。其次，当通过不同的以解释为导向的攻击进行测试时，METFA可以帮助捍卫香草，以及自适应的对抗性攻击，以防止解释。

In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network, we first design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously. More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification. It first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolve the conflicts between semantic and instance segmentation. Additionally, it handles the challenge caused by the varying number of instances and permits back propagation to the bottom modules in an end-to-end manner. Extensive experimental results on Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves stateof-the-art performance with much faster inference. Code has been made available at: https://github.com/ uber-research/UPSNet. * Equal contribution.† This work was done when Hengshuang Zhao was an intern at Uber ATG.