深度学习（DL）的快速增长和部署目睹了新兴的隐私和安全问题。为了减轻这些问题，已经讨论了安全的多方计算（MPC），以实现隐私保护DL计算。在实践中，它们通常是在很高的计算和沟通开销中，并有可能禁止其在大规模系统中的受欢迎程度。两种正交研究趋势吸引了人们对安全深度学习的能源效率的巨大兴趣，即MPC比较方案的高架降低和硬件加速度。但是，他们要么达到较低的减少比率，因此由于计算和通信节省有限而遭受了高潜伏期，或者是渴望的，因为现有的作品主要集中在CPU和GPU等一般计算平台上。在这项工作中，作为第一次尝试，我们通过将加密构件构建块的硬件延迟整合到DNN损耗功能中，以实现高能量效率，开发了一个系统的polympcnet，以减少MPC比较协议和硬件加速的联合额外降低的系统框架Polympcnet。和安全保证。我们的关键设计原理不是在DNN进行良好训练之后（通过删除或删除某些非物质操作员）训练（通过删除或删除某些非物质操作员）之后检查模型敏感性，而是要准确地执行DNN设计中的假设 - 培训DNN既是DNN都硬件有效且安全，同时逃脱了当地的最小值和鞍点并保持高精度。更具体地说，我们提出了通过多项式激活初始化方法直接提出的加密硬件友好的可训练多项式激活功能，以替代昂贵的2P-RELU操作员。我们开发了一个密码硬件调度程序和现场可编程门阵列（FPGA）平台的相应性能模型。

物联网设备越来越多地通过神经网络模型实施，以启用智能应用程序。从环境环境中收集能源的能源收集（EH）技术是电池可为这些设备供电的有前途的替代方法，因为维护成本较低和能源的广泛可用性。但是，能量收割机提供的功率很低，并且具有不稳定性的固有缺点，因为它随环境环境而变化。本文提出了EVE，EVE是一种自动化机器学习（AUTOML）共同探索框架，以搜索具有共享权重的所需的多模型，以进行能源收集的物联网设备。这些共享模型显着降低了记忆足迹，具有不同级别的模型稀疏性，延迟和准确性，以适应环境变化。进一步开发了有效的实施实施体系结构，以有效地执行设备上的每个模型。提出了一种运行时模型提取算法，该算法在触发特定模型模式时以可忽略的开销检索单个模型。实验结果表明，EVE生成的神经网络模型平均比没有修剪和共享的基线模型快2.5倍倍权重。

能量收集（EH）间歇性地运行的IOT设备，与深神经网络（DNN）的进步相结合，为实现可持续智能应用开辟了新的机会。然而，由于有限的资源和间歇电源导致频繁故障的挑战，实现了EH设备上的那些计算和内存密集型智能算法非常困难。为了解决这些挑战，本文提出了一种方法，使得具有用于微小能量收集装置的低能量加速器的超快速深度学习。我们首先提出了一种资源感知结构化DNN训练框架，它采用块循环矩阵与ADMM实现高压缩和模型量化，以利用各种矢量操作加速器的优点。然后提出了一种DNN实现方法，即采用低能量加速器来利用具有较小能耗的最大性能的低能量加速器。最后，我们进一步设计Flex，系统支持在能量收集情况下间歇性计算。来自三种不同DNN模型的实验结果表明RAD，ACE和FLEX可以对能源收集设备进行超快速和正确的推断，该设备可降低高达4.26倍的运行时间，高达7.7倍的能量降低，高精度在最高的状态下艺术。

随着实际量子计算机中的量子位数（QUBits）的数量恒定增加，实现和加速量子计算机上的普遍深入学习正在成为可能。随着这种趋势，基于量子神经元的不同设计出现了量子神经结构。 Quantum深度学习中的一个基本问题出现：什么是最好的量子神经结构？灵感来自古典计算的神经结构设计，该古典计算通常采用多种类型的神经元，本文首次尝试混合量子神经元设计来构建量子神经结构。我们观察到现有的量子神经元设计可能是完全不同但互补的，例如来自变分量子电路（VQC）和量子流的神经元。更具体地说，VQC可以应用真实值的权重，但遭受扩展到多个层，而量子流可以有效地构建多层网络，但仅限于使用二进制权重。要采取各自的优势，我们建议将它们混合在一起并弄清楚无缝连接的方法，而无需额外的昂贵测量。我们进一步研究了混合量子神经元的设计原理，这可以为未来提供量子神经结构勘探的指导。实验结果表明，具有混合量子神经元的鉴定的量子神经结构可以在MNIST数据集中达到90.62％的准确性，而VQC和量子流量分别比为52.77％和69.92％。

无线技术的最新进步使连接的自动驾驶汽车（CAV）能够通过车辆到车辆（V2V）通信收集有关其环境的信息。在这项工作中，我们为CAVS设计了基于信息共享的多代理增援学习（MARL）框架，以在做出决定以提高交通效率和安全性时利用额外的信息。我们提出的安全参与者批评算法有两种新技术：截断的Q功能和安全动作映射。截断的Q功能利用了来自相邻骑士的共享信息，以使Q-功能的联合状态和动作空间在我们的算法中不会在大型CAV系统中生长。我们证明了截短Q和全局Q函数之间近似误差的结合。安全的操作映射为基于控制屏障功能的培训和执行提供了可证明的安全保证。我们使用CARLA模拟器进行实验，我们表明我们的方法可以在不同的CAV比和不同的交通密度下的平均速度和舒适性方面提高CAV系统的效率。我们还表明，我们的方法避免执行不安全的动作，并始终保持与其他车辆的安全距离。我们构建了一个障碍物的场景，以表明共同的愿景可以帮助骑士早些时候观察障碍，并采取行动避免交通拥堵。

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.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.