与常规深层神经网络（DNN）相比，衍射光学神经网络（DONNS）在功率效率，并行性和计算速度方面具有显着优势，因此引起了很多关注，这些神经网络（DNN）在数字平台上实现时具有内在的限制。但是，反相反的算法训练的物理模型参数上具有离散值的现实世界光学设备是一个非平凡的任务，因为现有的光学设备具有非统一的离散级别和非单调属性。这项工作提出了一个新颖的设备对系统硬件软件代码框架，该框架可以对Donns W.R.T的有效物理意识培训进行跨层的任意实验测量的光学设备。具体而言，使用Gumbel-SoftMax来启用从现实世界设备参数的可区分映射到Donns的正向函数，在Donn中，Donn中的物理参数可以通过简单地最小化ML任务的损耗函数来训练。结果表明，我们提出的框架比传统的基于基于量化的方法具有显着优势，尤其是使用低精确的光学设备。最后，在低精度设置中，通过物理实验光学系统对所提出的算法进行了充分的验证。

加强学习（RL）在学术界和技术产业中获得了越来越多的吸引力，并推出了各种各样的有影响力的应用和产品。虽然研究正在积极地在许多方面进行（例如，离线RL，性能等），但许多RL从业者面临着基本忽略的挑战：确定设计的马尔可夫决策过程（MDP）是否有效和有意义。本研究提出了一种基于启发式的特征分析方法来验证MDP是否合理。我们认为，适合应用RL的MDP应包含一组状态特征，这些功能对动作和预测性依赖于奖励。我们在构造的环境中测试了我们的方法，表明我们的方法可以识别某些无效的环境制定。据我们所知，对RL问题配方进行有效性分析是一种新颖的方向。我们设想，我们的工具将作为一个动机示例，以帮助从业者更容易地将RL应用于现实世界问题。

在自主驾驶的复杂情况下，培训多个代理商以进行安全和合作的控制是一个挑战。对于一小群汽车，本文提出了麻木，这是一种培训多个代理商的新方法。 Lepus采用了一种纯粹的合作方式来培训多个代理，以策略网络的共享参数和多个代理的共享奖励函数为特色。特别是，Lepus通过对抗过程预先培训政策网络，提高其协作决策能力并进一步促进汽车驾驶的稳定性。此外，由于减轻了稀疏奖励的问题，Lepus通过结合随机网络和蒸馏网络从专家轨迹中学习了近似奖励功能。我们在Madras模拟平台上进行了广泛的实验。实验结果表明，通过麻法训练的多种代理可以避免同时驾驶时尽可能多的碰撞并超越其他四种方法，即DDPG-FDE，PSDDPG，MADDPG和MAGAIL和MAGAIL（DDPG）（DDPG）在稳定性方面。

实体集扩展（ESE）是一项有价值的任务，旨在找到给定种子实体描述的目标语义类别的实体。由于其发现知识的能力，各种NLP和下游应用程序都受益于ESE。尽管现有的引导方法取得了巨大进展，但其中大多数仍然依赖手动预定义的上下文模式。预定义的上下文模式的不可忽略的缺点是，它们不能灵活地推广到各种语义类别，我们将这种现象称为“语义敏感性”。为了解决这个问题，我们设计了一个上下文模式生成模块，该模块利用自回归语言模型（例如GPT-2）自动为实体生成高质量的上下文模式。此外，我们提出了GAPA，这是一种新型ESE框架，利用上述生成的模式扩展目标实体。对三个广泛使用的数据集进行了广泛的实验和详细分析，证明了我们方法的有效性。我们实验的所有代码都将用于可重复性。

多层erceptron（MLP），作为出现的第一个神经网络结构，是一个大的击中。但是由硬件计算能力和数据集的大小限制，它一旦沉没了数十年。在此期间，我们目睹了从手动特征提取到带有局部接收领域的CNN的范式转变，以及基于自我关注机制的全球接收领域的变换。今年（2021年），随着MLP混合器的推出，MLP已重新进入敏捷，并吸引了计算机视觉界的广泛研究。与传统的MLP进行比较，它变得更深，但改变了完全扁平化以补丁平整的输入。鉴于其高性能和较少的需求对视觉特定的感应偏见，但社区无法帮助奇迹，将MLP，最简单的结构与全球接受领域，但没有关注，成为一个新的电脑视觉范式吗？为了回答这个问题，本调查旨在全面概述视觉深层MLP模型的最新发展。具体而言，我们从微妙的子模块设计到全局网络结构，我们审查了这些视觉深度MLP。我们比较了不同网络设计的接收领域，计算复杂性和其他特性，以便清楚地了解MLP的开发路径。调查表明，MLPS的分辨率灵敏度和计算密度仍未得到解决，纯MLP逐渐发展朝向CNN样。我们建议，目前的数据量和计算能力尚未准备好接受纯的MLP，并且人工视觉指导仍然很重要。最后，我们提供了开放的研究方向和可能的未来作品的分析。我们希望这项努力能够点燃社区的进一步兴趣，并鼓励目前为神经网络进行更好的视觉量身定制设计。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

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.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.