主体组件分析（PCA）在给定固定组件维度的一类线性模型的情况下，将重建误差最小化。概率PCA通过学习PCA潜在空间权重的概率分布，从而创建生成模型，从而添加了概率结构。自动编码器（AE）最小化固定潜在空间维度的一类非线性模型中的重建误差，在固定维度处胜过PCA。在这里，我们介绍了概率自动编码器（PAE），该自动编码器（PAE）使用归一化流量（NF）了解了AE潜在空间权重的概率分布。 PAE快速且易于训练，并在下游任务中遇到小的重建错误，样本质量高以及良好的性能。我们将PAE与差异AE（VAE）进行比较，表明PAE训练更快，达到较低的重建误差，并产生良好的样品质量，而无需特殊的调整参数或培训程序。我们进一步证明，PAE是在贝叶斯推理的背景下，用于涂抹和降解应用程序的贝叶斯推断，可以执行概率图像重建的下游任务的强大模型。最后，我们将NF的潜在空间密度确定为有希望的离群检测度量。

We improve the understanding of the $\textit{golden ratio algorithm}$, which solves monotone variational inequalities (VI) and convex-concave min-max problems via the distinctive feature of adapting the step sizes to the local Lipschitz constants. Adaptive step sizes not only eliminate the need to pick hyperparameters, but they also remove the necessity of global Lipschitz continuity and can increase from one iteration to the next. We first establish the equivalence of this algorithm with popular VI methods such as reflected gradient, Popov or optimistic gradient descent-ascent in the unconstrained case with constant step sizes. We then move on to the constrained setting and introduce a new analysis that allows to use larger step sizes, to complete the bridge between the golden ratio algorithm and the existing algorithms in the literature. Doing so, we actually eliminate the link between the golden ratio $\frac{1+\sqrt{5}}{2}$ and the algorithm. Moreover, we improve the adaptive version of the algorithm, first by removing the maximum step size hyperparameter (an artifact from the analysis) to improve the complexity bound, and second by adjusting it to nonmonotone problems with weak Minty solutions, with superior empirical performance.

Recurrent neural networks (RNN) are the backbone of many text and speech applications. These architectures are typically made up of several computationally complex components such as; non-linear activation functions, normalization, bi-directional dependence and attention. In order to maintain good accuracy, these components are frequently run using full-precision floating-point computation, making them slow, inefficient and difficult to deploy on edge devices. In addition, the complex nature of these operations makes them challenging to quantize using standard quantization methods without a significant performance drop. We present a quantization-aware training method for obtaining a highly accurate integer-only recurrent neural network (iRNN). Our approach supports layer normalization, attention, and an adaptive piecewise linear (PWL) approximation of activation functions, to serve a wide range of state-of-the-art RNNs. The proposed method enables RNN-based language models to run on edge devices with $2\times$ improvement in runtime, and $4\times$ reduction in model size while maintaining similar accuracy as its full-precision counterpart.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.

With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions. Code is made publicly available at https://github.com/iprapas/landslide-sar-unet.

Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers.

用于在线状态估计的随机过滤器是自治系统的核心技术。此类过滤器的性能是系统能力的关键限制因素之一。此类过滤器的渐近行为（例如，用于常规操作）和瞬态响应（例如，对于快速初始化和重置）对于保证自主系统的稳健操作至关重要。本文使用n个方向测量值（包括车身框架和参考框架方向类型测量值）引入了陀螺仪辅助姿态估计器的新通用公式。该方法基于一种集成状态公式，该公式结合了导航，所有方向传感器的外部校准以及在单个模棱两可的几何结构中的陀螺式偏置状态。这种新提出的对称性允许模块化的不同方向测量及其外部校准，同时保持在同一对称性中包括偏置态的能力。随后使用此对称性的基于滤波器的估计量明显改善了瞬态响应，与最新方法相比，渐近偏置和外部校准估计。估计器在统计代表性的模拟中得到了验证，并在现实世界实验中进行了测试。

本文提出了一个贝叶斯框架，用于构建非线性，简约的浅层模型，用于多任务回归。提出的框架依赖于这样一个事实，即随机傅立叶特征（RFF）可以通过极端学习机器将RBF内核近似，其隐藏层由RFF形成。主要思想是将同一模型的两个双重视图结合在单个贝叶斯公式下，将稀疏的贝叶斯极限学习机器扩展到多任务问题。从内核方法的角度来看，提出的公式有助于通过RBF内核参数引入先前的域知识。从极端的学习机的角度来看，新的配方有助于控制过度拟合并实现简约的总体模型（服务每个任务的模型共享联合贝叶斯优化中选择的相同的RFF集合）。实验结果表明，在同一框架内将内核方法和极端学习机器的优势相结合可能会导致这两个范式中的每一个范式独立地取得的性能显着改善。

与简单英语的德国同行“莱希特·斯普拉奇（Leichte Sprache）”是一种旨在促进复杂的书面语言的受监管语言，否则不同的人群将无法访问。我们为简单德语 - 德语提供了一个新的与句子一致的单语语料库。它包含多个使用自动句子对准方法对齐的文档对准源。我们根据手动标记的对齐文档子集评估我们的对齐方式。通过F1得分衡量的句子对齐质量超过了先前的工作。我们根据CC BY-SA和MIT许可证的随附代码发布数据集。

基于脑部的事件的神经形态处理系统已成为一种有前途的技术，尤其是生物医学电路和系统。但是，神经网络的神经形态和生物学实现都具有关键的能量和记忆约束。为了最大程度地减少在多核神经形态处理器中的内存资源的使用，我们提出了一种受生物神经网络启发的网络设计方法。我们使用这种方法来设计针对小世界网络优化的新路由方案，同时介绍了一种硬件感知的放置算法，该算法优化了针对小型世界网络模型的资源分配。我们使用规范的小世界网络验证算法，并为其他网络提供初步结果