Publications

Simplified Concrete Dropout - Improving the Generation of Attribution Masks for Fine-grained Classification
Dimitri Korsch and Maha Shadaydeh and Joachim Denzler.
International Journal of Computer Vision. 2025.
[bibtex] [web] [abstract]

Seasonal Shifts in Plant Diversity Effects on Above-Ground-Below-Ground Phenological Synchrony
Ana E. Bonato Asato and Claudia Guimaraes-Steinicke and Gideon Stein and Berit Schreck and Teja Kattenborn and Anne Ebeling and Stefan Posch and Joachim Denzler and Tim Büchner and Maha Shadaydeh and Christian Wirth and Nico Eisenhauer and Jes Hines.
Journal of Ecology. 113 (2): pages 472-484. 2025.
[bibtex] [pdf] [web] [abstract]

Abstract: The significance of biological diversity as a mechanism that optimizes niche breadth for resource acquisition and enhancing ecosystem functionality is well-established. However, a significant gap remains in exploring temporal niche breadth, particularly in the context of phenological aspects of community dynamics. This study takes a unique approach by examining plant phenology, which has traditionally been focused on above-ground assessments, and delving into the relatively unexplored realm of below-ground processes. As a result, the influence of biological diversity on the synchronization of above-ground and below-ground dynamics is brought to the forefront, providing a novel perspective on this complex relationship. In this study, community traits (including plant height and greenness) and soil processes (such as root growth and detritivore feeding activity) were meticulously monitored at 2-week intervals over a year within an experimental grassland exhibiting a spectrum of plant diversity, ranging from monocultures to 60-species mixtures. Our findings revealed that plant diversity increased yearly plant height, root growth and detritivore feeding activity, while enhancing the synchrony between above-ground traits and soil dynamics. Soil microclimate also played a role in shaping the phenology of these traits and processes. However, plant diversity and soil microclimate on above-ground traits and soil dynamics effects varied considerably in strength and direction across seasons, indicating a nuanced relationship between biodiversity, climate and ecosystem processes. Notably, observations during the growing season unveiled a sequential pattern wherein peak plant community height preceded the onset of greenness. Meanwhile, root production commenced immediately after leaf senescence and persisted throughout winter. Although consistent throughout the year, detritivore activity exhibited pronounced peaks in the summer and late fall, albeit with notable variability. Synthesis. The study underscores the dynamic interplay between plant diversity, above-groundbelow-ground phenological patterns and ecosystem functioning. It suggests that plant diversity modulates above-groundbelow-ground interdependence through intricate phenological dynamics, with the degree of synchrony fluctuating in response to the varying combination of processes and seasonal changes. Thus, by providing comprehensive within-year data, the research elucidates the fundamental disparities in phenological patterns across shoots, roots and soil fauna activities, thereby emphasizing the pivotal role of plant diversity in shaping ecosystem processes.

Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach
Jonas Ziemer and Gideon Stein and Carolin Wicker and Jannik Jänichen and Daniel Kl\{"o}pper Katja and Last and Joachim Denzler and Christiane Schmullius and Maha Shadaydeh and Cl{\'e}mence Dubois.
Remote Sensing. 17 (6): 2025.
[bibtex] [pdf] [web] [abstract]

Abstract: Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R2 = 0.92) to as low as 0.05 mm using the full model search space (R2 = 0.99). Although the model accuracy for the PS datasets (MAEmax: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. This requires adequate dataset filtering to eliminate noisy PS points. Third, extended representations of water level and temperature, including interaction effects, can improve model accuracy and reduce prediction errors. With these insights, we recommend incorporating the proposed methodology into the monitoring program of gravity dams to enhance the accuracy in predicting their expected deformations.

Gradient Extrapolation for Debiased Representation Learning
Ihab Asaad and Maha Shadaydeh and Joachim Denzler.
. 2025.
[bibtex] [pdf] [abstract]

CausalRivers - Scaling Up Benchmarking of Causal Discovery for Real-world Time-series
Gideon Stein and Maha Shadaydeh and Jan Blunk and Niklas Penzel and Joachim Denzler.
International Conference on Learning Representations (ICLR). 2025.
[bibtex] [web] [abstract]

Abstract: 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.

Towards unobtrusive sleep stage classification in preterm infants using machine learning
Nathalie Demme and Maha Shadaydeh and Laura Schieder and Claus Doerfel and Stella Jhkel and Knut Holthoff and Hans Proquitt and Joachim Denzler and Jrgen Graf.
Biomedical Signal Processing and Control. 108: pages 107904. 2025.
[bibtex] [pdf] [web] [code] [supplementary] [abstract]

Directionality of Interpersonal Neural Influence in fNIRS Hyperscanning: Validation of a Spectral Causality Approach in a Motor Task
Maha Shadaydeh and Vanessa Nöring and Marcel Franz and Tara Chand and Ilona Croy and Joachim Denzler.
Authorea. 2025. (submitted to European Journal of Neuroscience)
[bibtex] [web] [abstract]

Regime Identification for Improving Causal Analysis in Non-stationary Timeseries
Wasim Ahmad and Maha Shadaydeh and Joachim Denzler.
arXiv preprint arXiv:2405.02315. 2024.
[bibtex] [pdf] [web] [abstract]

Deep-Learning Based Causal Inference: A Feasibility Study Based on Three Years of Tectonic-Climate Data From Moxa Geodynamic Observatory
Wasim Ahmad and Valentin Kasburg and Nina Kukowski and Maha Shadaydeh and Joachim Denzler.
Earth and Space Science. 11 (10): pages e2023EA003430. 2024.
[bibtex] [web] [abstract]

A Belowground Perspective on the Nexus Between Biodiversity Change, Climate Change, and Human Well-being
Nico Eisenhauer and Karin Frank and Alexandra Weigelt and Bartosz Bartkowski and R\'{e}my Beugnon and Katja Liebal and Miguel Mahecha and Martin Quaas and Djamil Al-Halbouni and Ana Bastos and Friedrich J. Bohn and Mariana Madruga de Brito and Joachim Denzler and Hannes Feilhauer and Rico Fischer and Immo Fritsche and Claudia Guimaraes-Steinicke and Martin H\"{a}nsel and Daniel B. M. Haun and Hartmut Herrmann and Andreas Huth and Heike Kalesse-Los and Michael Koetter and Nina Kolleck and Melanie Krause and Marlene Kretschmer and Pedro J. Leit{\~a}o and Torsten Masson and Karin Mora and Birgit M\"{u}ller and Jian Peng and Mira L. P\"{o}hlker and Leonie Ratzke and Markus Reichstein and Solveig Richter and Nadja R\"{u}ger and Beatriz S\'{a}nchez-Parra and Maha Shadaydeh and Sebastian Sippel and Ina Tegen and Daniela Thr\"{a}n and Josefine Umlauft and Manfred Wendisch and Kevin Wolf and Christian Wirth and Hannes Zacher and S\"{o}nke Zaehle and Johannes Quaas.
Journal of Sustainable Agriculture and Environment. 3 (2): pages e212108. 2024.
[bibtex] [web] [abstract]

Data-driven Prediction of Large Infrastructure Movements Through Persistent Scatterer Time Series Modeling
Gideon Stein and Jonas Ziemer and Carolin Wicker and Jannik Jänichen and Gabriele Demisch and Daniel Klöpper and Katja Last and Joachim Denzler and Christiane Schmullius and Maha Shadaydeh and Cl{\'e}mence Dubois.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pages 8669-8673. 2024.
[bibtex] [pdf] [abstract]

Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input
Maha Shadaydeh and Joachim Denzler and Mirco Migliavacca.
ICLR Workshop on Machine Learning for Remote Sensing (ICLR-WS). 2024.
[bibtex] [web] [abstract]

Deep Learning-based Group Causal Inference in Multivariate Time-series
Wasim Ahmad and Maha Shadaydeh and Joachim Denzler.
AAAI Workshop on AI for Time-series (AAAI-WS). 2024.
[bibtex] [pdf] [web] [abstract]

Investigating the Effects of Plant Diversity on Soil Thermal Diffusivity Using Physics- Informed Neural Networks
Gideon Stein and Sai Karthikeya Vemuri and Yuanyuan Huang and Anne Ebeling and Nico Eisenhauer and Maha Shadaydeh and Joachim Denzler.
ICLR Workshop on AI4DifferentialEquations In Science (ICLR-WS). 2024.
[bibtex] [pdf] [web]

Embracing the Black Box: Heading Towards Foundation Models for Causal Discovery from Time Series Data
Gideon Stein and Maha Shadaydeh and Joachim Denzler.
AAAI Workshop on AI for Time-series (AAAI-WS). 2024.
[bibtex] [pdf] [web] [abstract]

Plant Diversity Stabilizes Soil Temperature
Yuanyuan Huang and Gideon Stein and Olaf Kolle and Karl Kuebler and Ernst-Detlef Schulze and Hui Dong and David Eichenberg and Gerd Gleixner and Anke Hildebrandt and Markus Lange and Christiane Roscher and Holger Schielzeth and Bernhard Schmid and Alexandra Weigelt and Wolfgang W. Weisser and Maha Shadaydeh and Joachim Denzler and Anne Ebeling and Nico Eisenhauer.
bioRxiv. pages 2023-03. 2023.
[bibtex] [pdf]

Enhanced Stability of Grassland Soil Temperature by Plant Diversity
Yuanyuan Huang and Gideon Stein and Olaf Kolle and Karl Kuebler and Ernst-Detlef Schulze and Hui Dong and David Eichenberg and Gerd Gleixner and Anke Hildebrandt and Markus Lange and Christiane Roscher and Holger Schielzeth and Bernhard Schmid and Alexandra Weigelt and Wolfgang W. Weisser and Maha Shadaydeh and Joachim Denzler and Anne Ebeling and Nico Eisenhauer.
Nature Geoscience. pages 1-7. 2023.
[bibtex] [abstract]

Simplified Concrete Dropout - Improving the Generation of Attribution Masks for Fine-grained Classification
Dimitri Korsch and Maha Shadaydeh and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). 2023.
[bibtex] [pdf] [code] [supplementary] [abstract]

KI4KI: Neues Projekt zur regelmässigen {\"U}berwachung von Stauanlagen aus dem All
Clemence Dubois and Jannik Jänichen and Maha Shadaydeh and Gideon Stein and Alexandra Katz and Daniel Klöpper and Joachim Denzler and Christiane Schmullius and Katja Last.
Messtechnische {\"U}berwachung von Stauanlagen ; XII.Mittweidaer Talsperrentag. Pages 15-19. 2022.
[bibtex] [web] [abstract]

Outcome Prediction and Murmur Detection in Sets of Phonocardiograms by a Deep Learning-Based Ensemble Approach
Sven Festag and Gideon Stein and Tim Büchner and Maha Shadaydeh and Joachim Denzler and Cord Spreckelsen.
Computing in Cardiology (CinC). Pages 1-4. 2022.
[bibtex] [pdf] [abstract]

Sequential Causal Effect Variational Autoencoder: Time Series Causal Link Estimation under Hidden Confounding
Violeta Teodora Trifunov and Maha Shadaydeh and Joachim Denzler.
arXiv preprint arXiv:2209.11497. 2022.
[bibtex] [web] [abstract]

Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding
Maha Shadaydeh and Joachim Denzler and Mirco Migliavacca.
Engineering Proceedings. 18 (1): 2022.
[bibtex] [web] [abstract]

Causal Discovery using Model Invariance through Knockoff Interventions
Wasim Ahmad and Maha Shadaydeh and Joachim Denzler.
ICML Workshop on Spurious Correlations, Invariance and Stability (ICML-WS). 2022.
[bibtex] [pdf] [web] [abstract]

Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
Violeta Teodora Trifunov and Maha Shadaydeh and Joachim Denzler.
NeurIPS Workshop on A Causal View on Dynamical Systems (NeurIPS-WS). 2022.
[bibtex] [pdf] [web] [abstract]

A Comparison of Machine Learning Algorithms and Feature Sets for Automatic Vocal Emotion Recognition in Speech
Cem Dovgdu and Thomas Kessler and Dana Schneider and Maha Shadaydeh and Stefan R. Schweinberger.
Sensors. 22 (19): 2022.
[bibtex] [web] [abstract]

Counterfactual Generation with Knockoffs
Oana-Iuliana Popescu and Maha Shadaydeh and Joachim Denzler.
arXiv preprint arXiv:2102.00951. 2021.
[bibtex] [pdf] [web] [abstract]

A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Markus Reichstein and Joachim Denzler.
IEEE Access. 9: pages 107873-107883. 2021.
[bibtex] [web] [abstract]

Analyzing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study
Maha Shadaydeh and Lea Müller and Dana Schneider and Martin Thümmel and Thomas Kessler and Joachim Denzler.
IEEE Access. 9: pages 73780-73790. 2021.
[bibtex] [pdf] [web] [presentation] [abstract]

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
Violeta Teodora Trifunov and Maha Shadaydeh and Björn Barz and Joachim Denzler.
IEEE International Conference on Machine Learning and Applications (ICMLA). Pages 166-172. 2021.
[bibtex] [pdf] [web] [abstract]

Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Wasim Ahmad and Maha Shadaydeh and Joachim Denzler.
IEEE International Conference on Machine Learning and Applications (ICMLA). Pages 449-454. 2021.
[bibtex] [pdf] [web] [abstract]

Understanding Changes in Environmental Time Series with Time-frequency Causality Analysis
Maha Shadaydeh and Yanira Guanche Garcia and Miguel Mahecha and Joachim Denzler.
European Geosciences Union General Assembly (EGU): Abstract + Poster Presentation. 2020.
[bibtex] [pdf] [web] [abstract]

Abstract: Understanding causal effect relationships between the different variables in dynamical systems is an important and challenging problem in different areas of research such as attribution of climate change, brain neural connectivity analysis, psychology, among many others. These relationships are guided by the process generating them. Hence, detecting changes or new patterns in the causal effect relationships can be used not only for the detection but also for the diagnosis and attribution of changes in the underlying process.Time series of environmental time series most often contain multiple periodical components, e.g. daily and seasonal cycles, induced by the meteorological forcing variables. This can significantly mask the underlying endogenous causality structure when using time-domain analysis and therefore results in several spurious links. Filtering these periodic components as preprocessing step might degrade causal inference. This motivates the use of time-frequency processing techniques such as Wavelet or short-time Fourier transform where the causality structure can be examined at each frequency component and on multiple time scales.In this study, we use a parametric time-frequency representation of vector autoregressive Granger causality for causal inference. We first show that causal inference using time-frequency domain analysis outperforms time-domain analysis when dealing with time series that contain periodic components, trends, or noise. The proposed approach allows for the estimation of the causal effect interaction between each pair of variables in the system on multiple time scales and hence for excluding links that result from periodic components.Second, we investigate whether anomalous events can be identified based on the observed changes in causal relationships. We consider two representative examples in environmental systems: land-atmosphere ecosystem and marine climate. Through these two examples, we show that an anomalous event can indeed be identified as the event where the causal intensities differ according to a distance measure from the average causal intensities. Two different methods are used for testing the statistical significance of the causal-effect intensity at each frequency component.Once the anomalous event is detected, the driver of the event can be identified based on the analysis of changes in the obtained causal effect relationships during the time duration of the event and consequently provide an explanation of the detected anomalous event. Current research efforts are directed towards the extension of this work by using nonlinear state-space models, both statistical and deep learning-based ones.

A Virtual "Werkstatt" for Digitization in the Sciences
Sheeba Samuel and Maha Shadaydeh and Sebastian Böcker and Bernd Brügmann and Solveig F. Bucher and Volker Deckert and Joachim Denzler and Peter Dittrich and Ferdinand von Eggeling and Daniel Güllmar and Orlando Guntinas-Lichius and Birgitta König-Ries and Frank Löffler and Lutz Maicher and Manja Marz and Mirco Migliavacca and Jürgen R. Reichenbach and Markus Reichstein and Christine Römermann and Andrea Wittig.
Research Ideas and Outcomes. 6: pages e54106. 2020.
[bibtex] [web] [abstract]

Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework
Furrukh Sana and Tarig Ballal and Maha Shadaydeh and Ibrahim Hoteit and Tareq Y. Al-Naffouri.
Biomedical Signal Processing and Control. 48: pages 46-60. 2019.
[bibtex] [pdf] [web] [abstract]

Registration of High Resolution Sar and Optical Satellite Imagery Using Fully Convolutional Networks
Stefan Hoffmann and Clemens-Alexander Brust and Maha Shadaydeh and Joachim Denzler.
International Geoscience and Remote Sensing Symposium (IGARSS). Pages 5152-5155. 2019.
[bibtex] [pdf] [abstract]

Attribution of Multivariate Extreme Events
Yanira Guanche and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [abstract]

Time-Frequency Causal Inference Uncovers Anomalous Events in Environmental Systems
Maha Shadaydeh and Joachim Denzler and Yanira Guanche Garcia and Miguel Mahecha.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 499-512. 2019.
[bibtex] [pdf] [abstract]

Nonlinear Causal Link Estimation under Hidden Confounding with an Application to Time-Series Anomaly Detection
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 261-273. 2019.
[bibtex] [pdf] [abstract]

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Lea Müller and Maha Shadaydeh and Martin Thümmel and Thomas Kessler and Dana Schneider and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 490-497. 2019.
[bibtex] [pdf] [web] [abstract]

Causal Link Estimation under Hidden Confounding in Ecological Time Series
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [abstract]

Domain knowledge integration for causality analysis of carbon-cycle variables
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation. 2018.
[bibtex] [web] [abstract]

BACI: Towards a Biosphere Atmosphere Change Index - Detection of Extreme Events in the Biosphere
Yanira Guanche Garcia and Maha Shadaydeh and Miguel Mahecha and Markus Reichstein and Joachim Denzler.
BigSkyEarth Conference: AstroGeoInformatics. 2018.
[bibtex] [pdf] [web]

Classification of Spatiotemporal Marine Climate Patterns using Wavelet Coherence and Markov Random Field
Maha Shadaydeh and Yanira Guanche Garcia and Joachim Denzler.
American Geophysical Union Fall Meeting (AGU): Abstract + Oral Presentation. 2018.
[bibtex] [web] [abstract]

Abstract: Sea condition characterization and classification is a widely studied topic but rather challenging one due to the spatial and temporal variability in marine climate. The aim of this study is to develop a data-driven method for the classification of marine climate patterns. The proposed method consists of two main steps: i) Feature extraction applied to the time series of each point of the grid independently. ii) Spatiotemporal classification applied on the obtained features over the entire study area. The causal intensity between coupled marine variables can be efficiently visualized at different time-scales using wavelet coherence. To this end, we extract a set of features from the statistically significant wavelet coherence of each pair of the used marine variables: significant wave height (hs), mean wave period (Tm), and wave direction (\texttheta{}m). The obtained features, in addition to the sea level pressure (SLP), over the entire study area are then treated as multi-channel images. For the spatiotemporal classification of these images, we first apply the unsupervised K-means clustering method on the images of each three consecutive time instances. The K clusters represent K different marine climate patterns. Markov Random Fields (MRFs) provide an effective methodology for integrating spatiotemporal dependency between adjacent points into the image classification process. In this study, we use a MRF model for defining the spatiotemporal extent of the detected marine climate patterns, with the statistics of the detected K clusters used for the initial training. Experimental results show that the proposed method allows for a practical classification of marine climate into representative patterns that can be used for an accurate characterization of sea conditions, the analysis of extreme events and its impacts along the coast. A case study in the North Sea will be presented using the coasDat dataset [1]. [1] Weisse, R., H. v. Storch, U. Callies, A. Chrastansky, F. Feser, I. Grabemann, H. Guenther, A. Pluess, Th. Stoye, J. Tellkamp, J. Winterfeldt and K. Woth, (2008): Regional meteo-marine reanalyses and climate change projections: Results for Northern Europe and potentials for coastal and offshore applications. Bull. Amer. Meteor. Soc., 90, 849-860.

Causality analysis of ecological time series: a time-frequency approach
Maha Shadaydeh and Yanira Guanche Garcia and Miguel Mahecha and Markus Reichstein and Joachim Denzler.
International Workshop on Climate Informatics (CI). 2018.
[bibtex] [pdf]

Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model
Yanira Guanche Garcia and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
Natural Hazards. pages 1-19. 2018.
[bibtex] [pdf] [web] [abstract]

Abstract: Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five biosphere variables from a preliminary version of the Earth System Data Cube have been used in this study: Gross Primary Productivity, Latent Energy, Net Ecosystem Exchange, Sensible Heat and Terrestrial Ecosystem Respiration. To tackle the spatiotemporal dependencies of the biosphere variables, the proposed methodology after preprocessing the data is divided into two steps: a feature extraction step applied to each time series in the grid independently, followed by a spatiotemporal event detection step applied to the obtained novelty scores over the entire study area. The first step is based on the assumption that the time series of each variable can be represented by an autoregressive moving average (ARMA) process, and the anomalies are those time instances that are not well represented by the estimated ARMA model. The Mahalanobis distance of the ARMA models\~{A}\^{A}\textcent{}\~{A}\textcent{}\^{a}\mbox{\texteuro}{}\v{s}\^{A}\lnot{}\~{A}\textcent{}\^{a}\mbox{\texteuro}{}\v{z}\^{A}\textcent{} multivariate residuals is used as a novelty score. In the second step, the obtained novelty scores of the entire study are treated as time series of images. Markov random fields (MRFs) provide an effective and theoretically well-established methodology for integrating spatiotemporal dependency into the classification of image time series. In this study, the classification of the novelty score images into three classes, intense anomaly, possible anomaly, and normal, is performed using unsupervised K-means clustering followed by multi-temporal MRF segmentation applied recursively on the images of each consecutive \(L \ge \) 1 time steps. The proposed methodology was applied to an area covering Europe and Africa. Experimental results and validation based on known historic events show that the method is able to detect historic events and also provides a useful tool to define sensitive regions.

Analyzing the Time Variant Causality in Ecological Time Series: A Time-Frequency Approach
Maha Shadaydeh and Yanira Guanche Garcia and Miguel Mahecha and Markus Reichstein and Joachim Denzler.
International Conference on Ecological Informatics (ICEI). Pages 151-152. 2018.
[bibtex] [abstract]

Abstract: Attribution in ecosystems aims to identify the cause-effect relationships between the variables involved. The availability of high temporal resolution data along with the powerful computing platforms further enhance the capacity of data-driven methods in capturing the complex relationships between the variables of the underlying system. Time series of ecological variables most often contain different periodical components that can significantly mask the underling causality structure in time domain. This motivates the use of time-frequency processing techniques such as wavelet analysis or short time Fourier transform. In this study we present a time-frequency approach for causality analysis where the coupling between the variables is assumed to follow a locally time-invariant multivariate autoregressive (MVAR) model. We propose a sliding time window approach to examine the change of interactions, i.e. direction and strength of causality, between the different variables over seasons. The cause-effect relationships are extracted using the frequency domain representation of the MVAR Granger causality (MVAR-GC) [1,2] based on the generalized partial directed coherence (gPDC) [3]. We have first applied the proposed method to synthetic data to evaluate its sensitivity to different issues such as the selection of the model order, the sampling frequency, the absence of cause as well as the presence of non-linear coupling. The method is then applied to half-hourly meteorological observations and land flux eddy covariance data to investigate the causal-effect relationships between global radiation (Rg), air temperature (Tair), and the CO2 land fluxes: gross primary productivity (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco). The results show that time-frequency analysis based on MVAR-GC has promising potential in identifying the time variant causality structure within these variables along with the main time delay between different cause- effect pairs. Further research work is currently going for the investigation of the selection criteria of the model order, the sampling frequency, and the size of the time window at different time scales of causality analysis. This study is carried out within the framework of the project BACI which in part aims at developing an attribution scheme for changes in ecosystem functioning and studying the impacts of these changes on biodiversity patterns.

Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery
Maha Shadaydeh and Andras Zlinszky and Andrea Manno-Kovacs and Tamas Sziranyi.
International Journal of Remote Sensing. 38 (23): pages 7422-7440. 2017.
[bibtex] [pdf] [web] [abstract]

Image denoising via collaborative support-agnostic recovery
Muzammil Behzad and Mudassir Masood and Tarig Ballal and Maha Shadaydeh and Tareq. Y. Al-Naffouri.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Pages 1343-1347. 2017.
[bibtex] [web] [abstract]

Biosphere Anomalies Detection by Regression Models
Yanira Guanche and Maha Shadaydeh and Miguel Mahecha and Joachim Denzler.
Conference on Advances in Extreme Value Analysis and Application to Natural Hazards (EVAN). 2017.
[bibtex] [pdf]

Change Detection in Wetlands using Multi-layer Fusion Markov Random Field
Maha Shadaydeh and Andrea Manno-Kovacs and Tamas Sziranyi.
What can Remote Sensing do for the Conservation of Wetlands? - 1st International Symposium. 2015.
[bibtex]

Multilayer Markov Random Field models for change detection in optical remote sensing images
Csaba Benedek and Maha Shadaydeh and Zoltan Kato and Tamas Sziranyi and Josiane Zerubia.
ISPRS Journal of Photogrammetry and Remote Sensing. 107: pages 22-37. 2015.
[bibtex] [pdf] [web] [abstract]

An improved mutual information similarity measure for registration of multi-modal remote sensing images
Maha Shadaydeh and Tamas Sziranyi.
Proc. SPIE Remote Sensing. 9643: pages 9643-9643-7. 2015.
[bibtex] [web] [abstract]

An improved local similarity measure estimation for change detection in remote sensing images
Maha Shadaydeh and Tamas Sziranyi.
International Conference on Aerospace Electronics and Remote Sensing Technology. Pages 234-238. 2014.
[bibtex] [web] [abstract]

Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model
Tamas Sziranyi and Maha Shadaydeh.
IEEE Geoscience and Remote Sensing Letters. 11 (9): pages 1544-1548. 2014.
[bibtex] [pdf] [web] [abstract]

Improved segmentation of a series of remote sensing images by using a fusion MRF model
Tamas Sziranyi and Maha Shadaydeh.
International Workshop on Content-Based Multimedia Indexing (CBMI). Pages 137-142. 2013.
[bibtex] [web] [abstract]

New narrowband active noise control systems with significantly less computational requirements
Yegui Xiao and Maha Shadaydeh and Rabab K. Ward.
IEEE International Symposium on Circuits and Systems. Pages 41-44. 2008.
[bibtex] [web] [abstract]

Extraction of fetal ECG using adaptive Volterra filters
Maha Shadaydeh and Yegui Xiao and Rabab K. Ward.
European Signal Processing Conference. Pages 1-5. 2008.
[bibtex] [pdf] [web] [abstract]

Performance analysis of adaptive IIR notch filters based on least mean p-power error criterion
Maha Shadaydeh and Masayuki Kawamata.
International Symposium on Circuits and Systems (ISCAS). Pages 377-380. 2003.
[bibtex] [pdf] [web]

Steady StateAnalysis of the P-Power Algorithm for Constrained Adaptive IIR Notch Filter
Maha Shadaydeh and Yegui Xiao and Masayuki Kawawata.
IEEE Midwest Symposium on Circuits and Systems (MWSCAS). Pages 664-667. 2002.
[bibtex] [abstract]

Dynamical Behaviour Analysis of 2-D Adaptive digital Filters
Maha Shadaydeh. 1999. PhD Thesis
[bibtex] [pdf]

Bias Removal Algorithm for 2-D Equation Error Adaptive IIR Filters
Maha Shadaydeh and Masayuki Kawamata.
Multidimensional Systems and Signal Processing. 10 (4): pages 429-441. 1999.
[bibtex] [pdf] [web] [abstract]

Steady State Analysis of Two-Dimensional LMS Adaptive Filters Using the Independence Assumption
Maha Shadaydeh and Masayuki Kawamata.
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E82-A (3): 1998.
[bibtex] [pdf]

Convergence Analysis of 2-D Doubly-Indexed Block LMS Adaptive Filters
Maha Shadaydeh and Masayuki Kawawata.
IEEE International Workshop on Intelligent Signal Processing and Communication Systems. 1998.
[bibtex]

Identification of nonminimum phase FIR systems via fourth-order cumulants and genetic algorithm
Maha Shadaydeh and Yegui Xiao and Yoshiaki Tadokoro.
Signal Processing. 60 (3): pages 339-347. 1997.
[bibtex] [pdf] [web] [abstract]

Overdetermined C(q,k) formula using third and fourth order cumulants
Yegui Xiao and Maha Shadaydeh and Yoshiaki Tadokoro.
Electronics Letters. 32 (6): pages 601-603. 1996.
[bibtex] [web] [abstract]