Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key desired property of image generative models is the ability to disentangle different attributes, which should enable modification towards a style without changing the semantic content, and the modification parameters should generalize to different images. Previous studies have found that generative adversarial networks (GANs) are inherently endowed with such disentanglement capability, so they can perform disentangled image editing without re-training or fine-tuning the network. In this work, we explore whether diffusion models are also inherently equipped with such a capability. Our finding is that for stable diffusion models, by partially changing the input text embedding from a neutral description (e.g., "a photo of person") to one with style (e.g., "a photo of person with smile") while fixing all the Gaussian random noises introduced during the denoising process, the generated images can be modified towards the target style without changing the semantic content. Based on this finding, we further propose a simple, light-weight image editing algorithm where the mixing weights of the two text embeddings are optimized for style matching and content preservation. This entire process only involves optimizing over around 50 parameters and does not fine-tune the diffusion model itself. Experiments show that the proposed method can modify a wide range of attributes, with the performance outperforming diffusion-model-based image-editing algorithms that require fine-tuning. The optimized weights generalize well to different images. Our code is publicly available at https://github.com/UCSB-NLP-Chang/DiffusionDisentanglement.

Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the effectiveness of most traditional defence techniques. While at the same time, the remarkable performance of the machine learning methods, especially deep learning, in computer vision, had garnered research interests from the cyber security community to further enhance and automate intrusion detections. However, the expensive data labeling and limitation of anomalous data make it challenging to train an intrusion detector in a fully supervised manner. Therefore, intrusion detection based on unsupervised anomaly detection is an important feature too. In this paper, we propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.

One of the biggest challenges of natural language generation (NLG) is the proper handling of named entities. Named entities are a common source of grammar mistakes such as wrong prepositions, wrong article handling, or incorrect entity inflection. Without factoring linguistic representation, such errors are often underrepresented when evaluating on a small set of arbitrarily picked argument values, or when translating a dataset from a linguistically simpler language, like English, to a linguistically complex language, like Russian. However, for some applications, broadly precise grammatical correctness is critical -- native speakers may find entity-related grammar errors silly, jarring, or even offensive. To enable the creation of more linguistically diverse NLG datasets, we release a Corpus of Linguistically Significant Entities (CLSE) annotated by linguist experts. The corpus includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games. To demonstrate one possible use of CLSE, we produce an augmented version of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a small number of human translations, we create a linguistically representative NLG evaluation benchmark in three languages: French (high-resource), Marathi (low-resource), and Russian (highly inflected language). We establish quality baselines for neural, template-based, and hybrid NLG systems and discuss the strengths and weaknesses of each approach.

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.

队列智能或CI是这种新型优化算法之一。自成立以来，在很短的范围内成功地应用于各个领域，并且观察到与同类算法相比，其结果是有效的。到目前为止，在CI及其相关应用程序上还没有进行过这种类型的文献计量分析。因此，对于那些希望将CI提升到新水平的人来说，这篇研究论文将是破冰船。在这篇研究论文中，Scopus中可用的CI出版物通过图表，有关作者，源标题，关键字的网络图进行分析，这些年来，期刊和期刊。在某种程度上，该文献计量学论文以其文献计量详细信息来展示CI，其应用和详细的系统审查。

在不同的环境中进行了广泛的研究并实施了本体论操作，例如对齐和合并，例如，分类操作，关系代数，键入的图形语法，以及不同的关注。但是，在设置中对齐和合并操作共享某些通用属性，例如，分别由（i），（c），（a）和（r）标记为标记的掌握，交换性，关联性和代表性一个本体合并系统$（\ mathfrak {o}，\ sim，\ merge）$，其中$ \ mathfrak {o} $是相关的本体，$ \ sim $是$ \ mathfrak的二进制关系} $建模本体对齐和$ \ Merge $是$ \ Mathfrak {O} $建模本体合并的部分二进制操作。给定一个本体论存储库，有限集$ \ mathbb {o} \ subseteq \ mathfrak {o} $，其合并关闭$ \ widehat {\ mathbb {o}} $是最小的Ontologies关于合并的关闭。如果满足（i），（c），（a）和（r），则$ \ mathfrak {o} $和$ \ wideHat {\ mathbb {o}} $都自然地合并，$ \ WideHat {\ Mathbb {O}} $是有限的，可以有效地计算，包括分类，选择和查询某些特定元素，例如最大本体论和最小本体论。我们还表明，本体合并系统，由本体学给出$ v $对齐对和求职，满足属性：（i），（c），（a）和（r），以便部分订购合并系统，并且可以有效地计算出给定存储库相对于定位的合并关闭。

考虑以下优化问题：给定$ n \ times n $矩阵$ a $和$ \ lambda $，最大化$ \ langle a，u \ lambda u^*\ rangle $，其中$ u $ $ u $在unital Group $ \ mathrm上变化{u}（n）$。这个问题试图通过矩阵大约$ a $，其频谱与$ \ lambda $相同，并且通过将$ \ lambda $设置为适当的对角矩阵，可以恢复矩阵近似问题，例如pca和等级$ k $近似。我们研究了在使用用户的私人数据构建矩阵$ a $的设置中，为这种优化问题设计差异化私有算法的问题。我们给出有效的私有算法，在近似误差上带有上和下限。我们的结果统一并改进了有关私人矩阵近似问题的几项先前的作品。他们依靠格拉斯曼尼亚人的包装/覆盖数量范围扩展到应该具有独立利益的单一轨道。

机器学习（ML）可解释性技术可以揭示数据中的不良模式，这些模型模型开发以做出预测 - 一旦部署就会​​造成危害。但是，如何采取行动解决这些模式并不总是很清楚。在ML与人类计算机互动研究人员，医师和数据科学家之间的合作中，我们开发了GAM Changer，这是第一个互动系统，可帮助域专家和数据科学家轻松，负责任地编辑通用的添加剂模型（GAM）和修复有问题的模式。借助新颖的交互技术，我们的工具将可解释性置于行动中 - 使用户能够分析，验证和使模型行为与知识和价值相结合。医师已经开始使用我们的工具来调查和修复肺炎和败血症的风险预测模型，以及在不同领域工作的7位数据科学家的评估突出显示我们的工具易于使用，满足他们的模型编辑需求，并适合他们当前的工作流程。我们的工具以现代网络技术为基础，在用户的网络浏览器或计算笔记本电脑中本地运行，从而降低了使用的障碍。 GAM Changer可在以下公共演示链接中获得：https：//interpret.ml/gam-changer。

通常通过过去的选择来告知机器学习中的评估，例如要使用哪些数据集或指标。该标准化可以使用排行榜对平等基础进行比较，但是随着出现更好的替代方案，评估选择变得不佳。这个问题在自然语言生成中尤其相关，该语言需要不断改善的数据集，指标和人类评估以提出确定性的主张。为了使遵循最佳模型评估实践更加容易，我们介绍了GEMV2。新版本的一代，评估和指标基准为数据集，模型和指标开发人员提供了模块化基础架构，以使彼此受益。GEMV2支持40种记录的数据集中51种语言。所有数据集的模型都可以在线评估，我们的交互式数据卡创建和渲染工具使得在Living Benchmark中添加新数据集变得更加容易。

在均匀的Lipschitzness的简单假设下，即每样本样本梯度均匀地界限的大多数先前的收敛结果是在均匀的私有随机梯度下降（DP-SGD）中得出的。在许多问题，例如使用高斯数据的线性回归中，此假设是不现实的。我们可以通过假设每个样本梯度具有\ textit {样品依赖性}上限，即每样本的Lipschitz常数，而它们本身可能是无限的，那么我们就会放松均匀的唇。当按样本Lipschitz常数具有有限的矩时，我们在凸函数和非凸函数上得出DP-SGD的新收敛结果。此外，我们还提供了针对DP-SGD中选择剪辑标准的原则指导，以使其满足我们轻松的Lipschitzness的凸设置，而无需在Lipschitz常数上做出分配假设。我们通过基准测试数据集的实验来验证建议的有效性。