Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-wild evaluation of these methods is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under critical theoretical assumptions. Real-world causal structures, however, are often complex, evolving over time, non-linear, and influenced by unobserved factors, making it hard to decide on a proper causal discovery strategy. To bridge this gap, we introduce CausalRivers, the largest in-the-wild causal discovery benchmarking kit for time-series data to date. CausalRivers features an extensive dataset on river discharge that covers the eastern German territory (666 measurement stations) and the state of Bavaria (494 measurement stations). It spans the years 2019 to 2023 with a 15-minute temporal resolution. Further, we provide additional data from a flood around the Elbe River, as an event with a pronounced distributional shift. Leveraging multiple sources of information and time-series meta-data, we constructed two distinct causal ground truth graphs (Bavaria and eastern Germany). These graphs can be sampled to generate thousands of subgraphs to benchmark causal discovery across diverse and challenging settings. To demonstrate the utility of CausalRivers, we evaluate several causal discovery approaches through a set of experiments to identify areas for improvement. CausalRivers has the potential to facilitate robust evaluations and comparisons of causal discovery methods. Besides this primary purpose, we also expect that this dataset will be relevant for connected areas of research, such as time-series forecasting and anomaly detection. Based on this, we hope to push benchmark-driven method development that fosters advanced techniques for causal discovery, as is the case for many other areas of machine learning.

Abstract In fine-grained classification, which is classifying images into subcategories within a common broader category, it is crucial to have precise visual explanations of the classification model’s decision. While commonly used attention- or gradient-based methods deliver either too coarse or too noisy explanations unsuitable for highlighting subtle visual differences reliably, perturbation-based methods can precisely locate pixels causally responsible for the predicted category. The fill-in of the dropout (FIDO) algorithm is one of those methods, which utilizes concrete dropout (CD) to sample a set of attribution masks and updates the sampling parameters based on the output of the classification model. In this paper, we present a solution against the high variance in the gradient estimates, a known problem of the FIDO algorithm that has been mitigated until now by large mini-batch updates of the sampling parameters. First, our solution allows for estimating the parameters with smaller mini-batch sizes without losing the quality of the estimates but with a reduced computational effort. Next, our method produces finer and more coherent attribution masks. Finally, we use the resulting attribution masks to improve the classification performance on three fine-grained datasets without additional fine-tuning steps and achieve results that are otherwise only achieved if ground truth bounding boxes are used.

Deformation monitoring is a crucial task for dam operators, particularly given the rise in extreme weather events associated with climate change. Further, quantifying the expected deformations of a dam is a central part of this endeavor. Current methods rely on in situ data (i.e., water level and temperature) to predict the expected deformations of a dam (typically represented by plumb or trigonometric measurements). However, not all dams are equipped with extensive measurement techniques, resulting in infrequent monitoring. Persistent Scatterer Interferometry (PSI) can overcome this limitation, enabling an alternative monitoring scheme for such infrastructures. This study introduces a novel monitoring approach to quantify expected deformations of gravity dams in Germany by integrating the PSI technique with in situ data. Further, it proposes a methodology to find proper statistical representations in a data-driven manner, which extends established statistical approaches. The approach demonstrates plausible deformation patterns as well as accurate predictions for validation data (mean absolute error=1.81 mm), confirming the benefits of the proposed method.

Estimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges in accurate estimation of cause-effect relations. In this paper, we propose to use deep autoregressive networks (DeepAR) in tandem with counterfactual analysis to infer nonlinear causal relations in multivariate time series. We extend the concept of Granger causality using probabilistic forecasting with DeepAR. Since deep networks can neither handle missing input nor out-of-distribution intervention, we propose to use the Knockoffs framework (Barber and Candès, 2015) for generating intervention variables and consequently counterfactual probabilistic forecasting. Knockoff samples are independent of their output given the observed variables and exchangeable with their counterpart variables without changing the underlying distribution of the data. We test our method on synthetic as well as real-world time series datasets. Overall our method outperforms the widely used vector autoregressive Granger causality and PCMCI in detecting nonlinear causal dependency in multivariate time series.

There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Thus we develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We aim to answer the counterfactual question of would the anomalous event have occurred if the subset of the involved variables had been more similarly distributed to the data outside of the anomalous interval. Specifically, we detect anomalous intervals using the Maximally Divergent Interval (MDI) algorithm, replace a subset of variables with their in-distribution values within the detected interval and observe if the interval has become less anomalous, by re-scoring it with MDI. We evaluate our method on multivariate temporal and spatio-temporal data and confirm the accuracy of our anomaly attribution of multiple well-understood extreme climate events such as heatwaves and hurricanes.

Multi-modal image registration is a crucial step when fusing images which show different physical/chemical properties of an object. Depending on the compared modalities and the used registration metric, this process exhibits varying reliability. We propose a deep metric based on a fully convo-lutional neural network (FCN). It is trained from scratch on SAR-optical image pairs to predict whether certain image areas are aligned or not. Tests on the affine registration of SAR and optical images showing suburban areas verify an enormous improvement of the registration accuracy in comparison to registration metrics that are based on mutual information (MI).

In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data.

In a typical conventional narrowband ANC system, the discrete Fourier coefficients (DFC) for each frequency are estimated by a linear combiner. Each reference (cosine or sine) wave has to be filtered by an estimate of the secondary-path before it is fed to the LMS algorithm. We call this part x-filtering block. The number of x-filtering blocks is 2q where q is the number of targeted frequencies. For larger q and/or higher order (̂M) of estimated FIR-type secondary-path, the computational cost due to x-filtering may become a bottle-neck in real system implementation. Here, we propose a new narrowband ANC system structure which requires only two (2) x-filtering blocks regardless of q. All the cosine waves (or sine waves) are combined as an input to a x-filtering block. The output of this block is decomposed by an efficient bandpass filter bank into filtered-x cosine or sine waves for the FXLMS that follows. The computational cost of the new system is significantly reduced especially for large q and/or ̂M. The new structure is also modified to cope with the frequency mismatch (FM). Simulations demonstrate that the new systems present performance which is very similar to that of the conventional system, but enjoy great advantages in system implementation.