一个适合监视fr \'echet均值概念和变形模型概念的概念的框架。通用的含义是使用fr \'echet平均报价的概念来捕获数据的典型功能形状，而变形模型的概念允许对配置文件与典型形状的偏差进行可解释的参数化。基于功能数据的形状特征构建和提出了功能性EWMA型控制图，允许（a）识别与控制内部行为的转变，以及（b）提供潜在转移的因果关系，并具有某些定性特征的显着偏差（例如振幅或相变）。实施功能监测方案以评估环境空气污染。特别是，该方法实施到合成数据示例，以评估其在各种条件下的性能，并使用来自雅典市地区的传感器数据进行真实世界的示例，在该地区，空气污染物的概况及其特征成功地分析了，并且确定了控制外行为。

在本文中，我们首先提出了一种新的操作员外推（OE）方法，用于解决确定性变异不平等（VI）问题。类似于梯度（操作员）投影方法，OE通过在每次迭代中求解一个投影子问题来更新一个搜索序列。我们表明，OE可以以比现有方法更简单地解决各种VI问题的最佳收敛速率。然后，我们介绍随机操作员外推（SOE）方法，并建立其最佳收敛行为以解决不同的随机VI问题。特别是，SOE在文献中首次实现了解决基本问题的最佳复杂性，即，即随机平滑且强烈单调VI。我们还提出了一种随机块操作员外推（SBOE）方法，以进一步降低应用于具有一定块结构的大规模确定性VIS的OE方法的迭代成本。已经进行了数值实验，以证明所提出算法的潜在优势。实际上，所有这些算法都用于求解概括的单调变异不平等（GMVI）问题，其操作员不一定是单调的。我们还将在同伴论文中讨论基于OE的最佳政策评估方法。

在这项工作中，对于不确定和结构化数据的聚类计划被认为依赖于Wasserstein Barycenters的概念，并伴随着基于Wasserstein空间的内在几何形状的适当聚类指数，在该几何形状上执行了群集任务。这种类型的聚类方法在许多领域都高度赞赏，在观察/实验误差很大（例如，天文学，生物学，遥感等）或数据性质更为复杂，并且传统学习算法不适用或有效治疗，因此高度赞赏。它们（例如网络数据，间隔数据，高频记录，矩阵数据等）。从这个角度来看，每个观察结果都是通过适当的概率度量来确定的，并且提出的聚类方案依赖于歧视标准，这些标准通过最佳运输理论从概率测量方面利用了概率测量空间的几何结构。通过模拟研究和两个现实世界应用中的实施来说明拟议方法和地球标准性能的优势和能力：（a）根据其观察到的政府债券收益率曲线和（b）对欧元区国家的群集进行分类，并（b）对到某些土地的卫星图像使用类别，这是遥感中的标准任务。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited. In this work we propose M2DS2, a simple and sample-efficient finetuning strategy for large pretrained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a $120$ hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when a only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels after being trained on less than 50 pre-existing labeled images. Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth estimation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model development which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation.