随机近似算法是迭代过程，用于在目标未知且直接观察结果被噪声损坏的环境中近似目标值。例如，当目标函数或模型不直接知道时，这些算法对于根找到和最小化是有用的。最初是在Robbins和Monro的1951年论文中引入的，随机近似领域已大大增长，并影响了从自适应信号处理到人工智能的应用领域。例如，在机器学习的各个子域中无处不在的随机梯度下降算法是基于随机近似理论。在本文中，我们为由于Aryeh dvoretzky的一般融合定理提供了正式的证明（在COQ证明助手中），这意味着重要的经典方法（例如Robbins-Monro和Kiefer-Wolfowitz算法）的收敛性。在此过程中，我们构建了一个综合的量子库库理论概率理论和随机过程。

Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions of ToN-IoT dataset and point out inconsistencies in some versions. We filter the full version of ToN-IoT and present a new version labeled ToN-IoT-R. To ensure generalization we propose a new standardized and compact set of flow features which are derived solely from NetFlowv5-compatible data. We separate numeric data and flags into different categories and propose a new dataset-agnostic normalization approach for numeric features. This allows us to preserve meaning of flow flags and we propose to conduct targeted analysis based on, for instance, network protocols. For flow classification we use E-GraphSage algorithm with modified node initialization technique that allows us to add node degree to node features. We achieve high classification accuracy on ToN-IoT-R and compare it with previously published results for ToN-IoT, NF-ToN-IoT, and NF-ToN-IoT-v2. We highlight the importance of careful data collection and labeling and appropriate data preprocessing choice and conclude that the proposed set of features is more applicable for real NIDS due to being less demanding to traffic monitoring equipment while preserving high flow classification accuracy.

In atomistic simulations of solids, ability to classify crystal phases and lattice defects in the presence of thermal fluctuations is essential for gaining deeper insights into the simulated dynamics. The need for accurate and efficient characterization methods is especially acute in presently emerging large-scale simulations of multi-phase systems far from equilibrium. Taking the perspective that delineating order and disorder features from ubiquitous thermal vibrations is akin to extracting signal from noise, we consider classification of ordered phases and identification of disordered crystal defects to be fundamentally the same problem and address them both with a unified approach: a denoising score function that removes thermal noise and recovers any underlying crystalline order-disorder. Built on a rotationally equivariant graph neural network (NequIP), the denoiser was trained entirely with synthetically noised structures and requires no simulation data during training. To demonstrate its denoising capabilities, the denoiser is shown to effectively remove thermal vibrations of BCC, FCC, and HCP crystal structures without impacting the underlying disordered defects, including point defects, dislocations, grain boundaries, and liquid disorder. In particular the denoiser was applied to two relatively complex MD simulations that present practical challenges: a Cu solidification trajectory involving a polymorphic nucleus, and a trajectory of BCC Ta undergoing plastic deformation resulting in dislocation networks and point defect clusters. In both cases the denoiser facilitates or trivializes the subsequent characterization of the order-disorder features. Lastly, we outline future work to extend our denoising model to more complex crystal structures and to multi-element systems.

事实证明，诸如层归一化（LN）和批处理（BN）之类的方法可有效改善复发性神经网络（RNN）的训练。但是，现有方法仅在一个特定的时间步骤中仅使用瞬时信息进行归一化，而归一化的结果是具有时间无关分布的预反应状态。该实现无法解释RNN的输入和体系结构中固有的某些时间差异。由于这些网络跨时间步骤共享权重，因此也可能需要考虑标准化方案中时间步长之间的连接。在本文中，我们提出了一种称为“分类时间归一化”（ATN）的归一化方法，该方法保留了来自多个连续时间步骤的信息，并使用它们归一化。这种设置使我们能够将更长的时间依赖项引入传统的归一化方法，而无需引入任何新的可训练参数。我们介绍了梯度传播的理论推导，并证明了权重缩放不变属性。我们将ATN应用于LN的实验表明，对各种任务（例如添加，复制和DENOISE问题和语言建模问题）表现出一致的改进。

在这些注释中，我们为具有随机初始条件的部分微分方程开发了路径积分方法。然后，我们将其应用于尖峰张量模型的动力学，并表明大$ N $鞍点方程由Melonic型图主导。

近年来，使用正交矩阵已被证明是通过训练，稳定性和收敛尤其是控制梯度来改善复发性神经网络（RNN）的一种有希望的方法。通过使用各种门和记忆单元，封闭的复发单元（GRU）和长期短期记忆（LSTM）体系结构解决了消失的梯度问题，但它们仍然容易出现爆炸梯度问题。在这项工作中，我们分析了GRU中的梯度，并提出了正交矩阵的使用，以防止梯度问题爆炸并增强长期记忆。我们研究了在哪里使用正交矩阵，并提出了基于Neumann系列的缩放尺度的Cayley转换，以训练GRU中的正交矩阵，我们称之为Neumann-cayley Orthoconal orthoconal Gru或简单的NC-GRU。我们介绍了有关几个合成和现实世界任务的模型的详细实验，这些实验表明NC-GRU明显优于GRU以及其他几个RNN。

本文介绍了STC有限公司的描述，该系统提交给NIST 2021扬声器识别评估，用于固定和开放的培训条件。这些系统由许多不同的子系统组成，基于使用深神经网络作为特征提取器。在NIST 2021 SRE挑战期间，我们专注于培训最先进的深部扬声器嵌入式提取器，如Contive角度裕度的损耗功能。此外，通过自动语音识别中的Wav2Vec 2.0特征的最近成功的启发，我们探讨了这种方法对提交的扬声器验证的有效性。根据我们的观察，预先训练的大wave2vec 2.0模型的微调为开放式条件提供了最佳的开展系统。我们对固定条件的WAV2VEC 2.0提取器的实验表明，与对比预测编码损失的无监督自回归预测将打开从原始语音信号训练强大的变压器的提取器。对于视频模型，我们通过RetinaFace面部探测器和深签名脸部嵌入式提取器开发了我们的最佳解决方案，培训了大面孔图像数据集。主要系统的最终结果是通过在分数水平上的不同配置融合的不同配置而获得，然后进行评分校准。