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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="friedrich2020hybrid"><span style="font-variant: small-caps">Friedrich, M., Illium, S., Fayolle, P.-A., and Linnhoff-Popien, C.</span> 2020. A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds. <i>15th International Joint Conference on Computer Graphics Theory and Applications</i>.</span>
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data-bibtex-json='"@inproceedings{friedrich2020hybrid,\n title = {A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds},\n author = {Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},\n booktitle = {15th International Joint Conference on Computer Graphics Theory and Applications},\n year = {2020}\n}\n"'
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data-bibtex-json='"@inproceedings{friedrich2020hybrid,\n title = {A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds},\n author = {Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},\n booktitle = {15th International Joint Conference on Computer Graphics Theory and Applications},\n year = {2020}\n}\n"'
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<center>
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<img src="/assets/figures/4_point_cloud_pipeline.jpg" alt="Diagram illustrating the hybrid point cloud segmentation pipeline" style="display:block; width:100%" />
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<figcaption>Overview of the hybrid segmentation and primitive fitting pipeline.</figcaption>
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<p><br /></p>
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<p>This research addresses challenges in accurately segmenting large-scale 3D point clouds into meaningful geometric primitives, specifically spheres, cylinders, and cuboids. Existing methods often struggle with scalability or robustness when faced with diverse shapes and noisy real-world data.</p>
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<p>We propose a novel <strong>hybrid approach</strong> that synergistically combines multiple techniques to overcome these limitations:</p>
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<ol>
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<li><strong>Deep Learning Integration:</strong> Utilized to potentially enhance initial feature extraction or provide guidance for subsequent steps (the exact role should be clarified based on the paper, e.g., pre-segmentation, feature learning).</li>
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<li><strong>RANSAC-based Primitive Fitting:</strong> Employs the robust RANSAC algorithm for accurately fitting simpler geometric shapes like spheres and cylinders to subsets of the point cloud.</li>
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<li><strong>DBSCAN Clustering:</strong> Applied for grouping remaining points or refining segmentation boundaries, effectively handling noise and varying point densities.</li>
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<li><strong>Specialized Genetic Algorithm:</strong> A custom Genetic Algorithm is introduced specifically for the robust detection and fitting of cuboid primitives, which are often challenging for standard fitting methods.</li>
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</ol>
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<p>This integrated pipeline demonstrates enhanced stability and robustness compared to methods relying on a single technique. It particularly excels in reconstructing the target primitives from large and complex point sets. The effectiveness of the approach is validated through quantitative performance metrics and qualitative visualizations, with a discussion acknowledging the method’s scope and potential limitations. For a detailed technical description and evaluation, please refer to the publication by <a class="citation" href="/publications#friedrich2020hybrid">[Friedrich et al. 2020]</a>.</p>
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<img src="/assets/figures/4_point_cloud_segmentation.jpg" alt="Example result showing a point cloud segmented into different colored geometric primitives" style="display:block; width:890%" />
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<figcaption>Example segmentation result demonstrating primitive identification.</figcaption>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2020-05-01T00:00:00+02:00">May 1, 2020</time></p>
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<ol class="bibliography"><li><span id="muller2021acoustic"><span style="font-variant: small-caps">Müller, R., Illium, S., Ritz, F., et al.</span> 2020. Acoustic leak detection in water networks. <i>arXiv preprint arXiv:2012.06280</i>.</span>
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data-bibtex-json='"@article{muller2021acoustic,\n title = {Acoustic leak detection in water networks},\n author = {Müller, Robert and Illium, Steffen and Ritz, Fabian and Schröder, Tobias and Platschek, Christian and Ochs, Jörg and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2012.06280},\n year = {2020}\n}\n"'
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data-bibtex-json='"@article{muller2021acoustic,\n title = {Acoustic leak detection in water networks},\n author = {Müller, Robert and Illium, Steffen and Ritz, Fabian and Schröder, Tobias and Platschek, Christian and Ochs, Jörg and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2012.06280},\n year = {2020}\n}\n"'
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\10_water_networks_approach.jpg" alt="Diagram illustrating the anomaly detection approach for leak detection" /></p>
|
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|
||||
<p>Detecting leaks in vast municipal water distribution networks is critical for resource conservation and infrastructure maintenance. This study introduces and evaluates an <strong>anomaly detection approach for acoustic leak identification</strong>, specifically designed with <strong>energy efficiency</strong> and <strong>ease of deployment</strong> as key considerations.</p>
|
||||
|
||||
<p>The methodology leverages acoustic recordings captured by microphones deployed directly on a section of a real-world <strong>municipal water network</strong>. Instead of requiring continuous monitoring, the proposed system mimics human inspection routines by performing <strong>intermittent checks</strong>, significantly reducing power consumption and data load.</p>
|
||||
|
||||
<p>Various <strong>anomaly detection models</strong>, ranging from traditional “shallow” methods (e.g., GMMs, OC-SVMs) to more complex <strong>deep learning architectures</strong> (e.g., autoencoders, potentially CNNs on spectrograms), were trained using data representing normal network operation. These models were then evaluated on their ability to distinguish anomalous sounds indicative of leaks.</p>
|
||||
|
||||
<p>Key findings include:</p>
|
||||
<ul>
|
||||
<li>Detecting leaks occurring acoustically <strong>nearby</strong> the sensor proved relatively straightforward for most evaluated models.</li>
|
||||
<li><strong>Neural network-based methods demonstrated superior performance</strong> in identifying leaks originating <strong>further away</strong> from the sensor, showcasing their ability to capture more subtle acoustic signatures amidst background noise.</li>
|
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|
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<img src="/assets/figures/10_water_networks_mel.jpg" alt="Mel-spectrogram examples showing acoustic signatures of normal operation versus leak sounds" style="display:block; width:90%" />
|
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<figcaption>Mel-spectrogram visualizations comparing normal sounds and leak-related acoustic patterns.</figcaption>
|
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|
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<p><br /></p>
|
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|
||||
<p>This research validates the feasibility of using anomaly detection for practical, energy-efficient acoustic leak monitoring in water networks, highlighting the advantages of deep learning techniques for detecting more challenging, distant leaks. <a class="citation" href="/publications#muller2021acoustic">[Müller et al. 2020]</a></p>
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<ol class="bibliography"><li><span id="muller2020analysis"><span style="font-variant: small-caps">Müller, R., Illium, S., Ritz, F., and Schmid, K.</span> 2020. Analysis of feature representations for anomalous sound detection. <i>arXiv preprint arXiv:2012.06282</i>.</span>
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\8_anomalous_sound_features.jpg" alt="Diagram showing features extracted by different pretrained networks visualized in a latent space" /></p>
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||||
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||||
<p>Detecting anomalous sounds, particularly in industrial settings, is crucial for predictive maintenance and safety. This often involves unsupervised or semi-supervised approaches where models learn a representation of ‘normal’ sounds. This research explores the effectiveness of leveraging <strong>transfer learning</strong> for this task by using <strong>pretrained deep neural networks</strong> as fixed feature extractors.</p>
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||||
<p>The core methodology involves:</p>
|
||||
<ol>
|
||||
<li>Taking pretrained networks trained on large datasets from various domains.</li>
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||||
<li>Using these networks to extract high-level, potentially semantically rich feature vectors from audio signals (specifically, industrial machine noises relevant to challenges like DCASE - Detection and Classification of Acoustic Scenes and Events).</li>
|
||||
<li>Modeling the distribution of features extracted from ‘normal’ sounds using a <strong>Gaussian Mixture Model (GMM)</strong>.</li>
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<li>Identifying anomalous sounds as those whose extracted features have low likelihood under the learned normality model.</li>
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<p>A key aspect of this study was comparing feature extractors pretrained on distinctly different domains:</p>
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<ul>
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||||
<li><strong>Images</strong> (e.g., models trained on ImageNet)</li>
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||||
<li><strong>Environmental Sounds</strong> (e.g., models trained on AudioSet or ESC-50)</li>
|
||||
<li><strong>Music</strong> (e.g., models trained on music tagging datasets)</li>
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</ul>
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<p>These were evaluated alongside a baseline autoencoder trained directly on the target machine sound data.</p>
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||||
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||||
<p>Surprisingly, the results indicated that features derived from networks pretrained on <strong>music data</strong> often yielded the best performance in detecting anomalous industrial sounds, frequently surpassing features from environmental sound models and the autoencoder baseline. This counter-intuitive finding suggests that direct domain similarity between the pretraining dataset and the target application data is not necessarily the primary factor determining the utility of transferred features for anomaly detection. <a class="citation" href="/publications#muller2020analysis">[Müller et al. 2020]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-01T00:00:00+01:00">March 1, 2021</time></p>
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<ol class="bibliography"><li><span id="kolle2024aquarium"><span style="font-variant: small-caps">Kölle, M., Erpelding, Y., Ritz, F., Phan, T., Illium, S., and Linnhoff-Popien, C.</span> 2024. Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms. <i>arXiv preprint arXiv:2401.07056</i>.</span>
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data-bibtex-json='"@article{kolle2024aquarium,\n title = {Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms},\n author = {Kölle, Michael and Erpelding, Yannick and Ritz, Fabian and Phan, Thomy and Illium, Steffen and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2401.07056},\n year = {2024}\n}\n"'
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data-bibtex-json='"@article{kolle2024aquarium,\n title = {Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms},\n author = {Kölle, Michael and Erpelding, Yannick and Ritz, Fabian and Phan, Thomy and Illium, Steffen and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2401.07056},\n year = {2024}\n}\n"'
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<p><img src="\assets\figures\20_aquarium.png" alt="Diagram illustrating the multi-agent reinforcement learning cycle within the Aquarium environment" style="display:block; width:40%" class="align-right" /></p>
|
||||
|
||||
<p>The study of complex interactions using Multi-Agent Reinforcement Learning (MARL), particularly <strong>predator-prey dynamics</strong>, often requires specialized simulation environments. To streamline research and avoid redundant development efforts, we introduce <strong>Aquarium</strong>: a versatile, open-source MARL environment specifically designed for investigating predator-prey scenarios and related <strong>emergent behaviors</strong>.</p>
|
||||
|
||||
<p>Key Features of Aquarium:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>Framework Integration:</strong> Built upon and seamlessly integrates with the popular <strong>PettingZoo API</strong>, allowing researchers to readily apply existing MARL algorithm implementations (e.g., from Stable-Baselines3, RLlib).</li>
|
||||
<li><strong>Physics-Based Movement:</strong> Simulates agent movement on a two-dimensional, continuous plane with edge-wrapping boundaries, incorporating basic physics for more realistic interactions.</li>
|
||||
<li><strong>High Customizability:</strong> Offers extensive configuration options for:
|
||||
<ul>
|
||||
<li><strong>Agent-Environment Interactions:</strong> Observation spaces, action spaces, and reward functions can be tailored to specific research questions.</li>
|
||||
<li><strong>Environmental Parameters:</strong> Key dynamics like agent speeds, prey reproduction rates, predator starvation mechanisms, sensor ranges, and more are fully adjustable.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><strong>Visualization & Recording:</strong> Includes a resource-efficient visualizer and supports video recording of simulation runs, facilitating qualitative analysis and understanding of agent behaviors.</li>
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|
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<img src="/assets/figures/20_observation_vector.png" alt="Diagram detailing the construction of the observation vector for an agent" style="display:inline-table; width:85%" />
|
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<figcaption>Construction details of the agent observation vector.</figcaption>
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<p>To demonstrate its capabilities, we conducted preliminary studies using <strong>Proximal Policy Optimization (PPO)</strong> to train multiple prey agents learning to evade a predator within Aquarium. Consistent with findings in existing MARL literature, our results showed that training agents with <strong>individual policies led to suboptimal performance</strong>, whereas utilizing <strong>parameter sharing</strong> among prey agents significantly improved coordination, sample efficiency, and overall evasion success. <a class="citation" href="/publications#kolle2024aquarium">[Kölle et al. 2024]</a></p>
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<a href="http://localhost:4000/research/audio-vision-transformer/" itemprop="url">Audio Vision Transformer
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<ol class="bibliography"><li><span id="illium2021visual"><span style="font-variant: small-caps">Illium, S., Müller, R., Sedlmeier, A., and Popien, C.-L.</span> 2021. Visual Transformers for Primates Classification and Covid Detection. <i>Proc. Interspeech 2021</i>, 451–455.</span>
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data-bibtex-json='"@article{illium2021visual,\n title = {Visual Transformers for Primates Classification and Covid Detection},\n author = {Illium, Steffen and Müller, Robert and Sedlmeier, Andreas and Popien, Claudia-Linnhoff},\n journal = {Proc. Interspeech 2021},\n pages = {451--455},\n year = {2021}\n}\n"'
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data-bibtex-json='"@article{illium2021visual,\n title = {Visual Transformers for Primates Classification and Covid Detection},\n author = {Illium, Steffen and Müller, Robert and Sedlmeier, Andreas and Popien, Claudia-Linnhoff},\n journal = {Proc. Interspeech 2021},\n pages = {451--455},\n year = {2021}\n}\n"'
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<p>This research explores the application of the <strong>Vision Transformer (ViT)</strong> architecture, originally designed for image processing, to the domain of audio classification by operating on <strong>mel-spectrogram representations</strong>. The ViT’s attention mechanisms offer a potentially powerful alternative to convolutional approaches for capturing relevant patterns in spectrogram data.</p>
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<img src="/assets/figures/12_vision_transformer_models.jpg" alt="Diagram illustrating the Vision Transformer architecture adapted for mel-spectrogram input" style="display:block; width:80%" />
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<figcaption>Adapting the Vision Transformer architecture for processing mel-spectrograms.</figcaption>
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<p><br /></p>
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<p>Key aspects of the methodology include:</p>
|
||||
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<ul>
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||||
<li><strong>ViT Adaptation:</strong> Applying the ViT model directly to mel-spectrograms treated as images.</li>
|
||||
<li><strong>Data Augmentation:</strong> Employing <strong>mel-based data augmentation</strong> techniques (e.g., SpecAugment variants) to improve model robustness and generalization.</li>
|
||||
<li><strong>Sample Weighting:</strong> Utilizing sample weighting strategies to address potential class imbalances or focus on specific aspects of the dataset.</li>
|
||||
<li><strong>Patching Strategy:</strong> Introducing and evaluating an <strong>overlapping vertical patching</strong> method, potentially better suited for capturing temporal structures in spectrograms compared to standard non-overlapping patches.</li>
|
||||
</ul>
|
||||
|
||||
<p>The effectiveness of this “Mel-Vision Transformer” approach was demonstrated within the context of the <strong>ComParE 2021 (Computational Paralinguistics Challenge)</strong>. The proposed model achieved notable performance, <strong>surpassing many established single-model baseline results</strong> on the challenge tasks.</p>
|
||||
|
||||
<p>Furthermore, the study includes an analysis of different parameter configurations and architectural choices, providing insights into optimizing ViT models for audio processing tasks. This work showcases the adaptability and potential of transformer architectures, particularly ViT, for effectively tackling audio classification challenges. <a class="citation" href="/publications#illium2021visual">[Illium et al. 2021]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-05T00:00:00+01:00">March 5, 2021</time></p>
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<ol class="bibliography"><li><span id="kolle2023compression"><span style="font-variant: small-caps">Kölle, M., Illium, S., Hahn, C., Schauer, L., Hutter, J., and Linnhoff-Popien, C.</span> 2023. Compression of GPS Trajectories using Autoencoders. <i>arXiv preprint arXiv:2301.07420</i>.</span>
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data-bibtex-json='"@article{kolle2023compression,\n title = {Compression of GPS Trajectories using Autoencoders},\n author = {Kölle, Michael and Illium, Steffen and Hahn, Carsten and Schauer, Lorenz and Hutter, Johannes and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2301.07420},\n year = {2023}\n}\n"'
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data-bibtex-json='"@article{kolle2023compression,\n title = {Compression of GPS Trajectories using Autoencoders},\n author = {Kölle, Michael and Illium, Steffen and Hahn, Carsten and Schauer, Lorenz and Hutter, Johannes and Linnhoff-Popien, Claudia},\n journal = {arXiv preprint arXiv:2301.07420},\n year = {2023}\n}\n"'
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<p>The proliferation of location-aware mobile devices generates vast amounts of GPS trajectory data, necessitating efficient storage solutions. While various compression techniques aim to reduce data volume, preserving essential spatio-temporal information remains crucial.</p>
|
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<center>
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<img src="/assets/figures/23_trajectory_model.png" alt="Schematic diagram of the LSTM autoencoder model architecture used for trajectory compression" style="display:block; width:60%" />
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<p>This paper introduces a novel approach for <strong>compressing and reconstructing GPS trajectories</strong> using a <strong>Long Short-Term Memory (LSTM) autoencoder</strong>. The autoencoder learns a compressed latent representation of the trajectory sequence, which can then be decoded to reconstruct the original path.</p>
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<p><img src="\assets\figures\23_trajectory_scores.png" alt="Graphs showing mean distance errors (e.g., Fréchet, DTW) for different compression ratios on the T-Drive dataset" style="display:block; width:50%" class="align-right" /></p>
|
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<p>Our method was evaluated on two distinct datasets: one from a gaming context and another real-world dataset (T-Drive). We assessed performance across a range of compression ratios and trajectory lengths, comparing it against the widely used traditional <strong>Douglas-Peucker algorithm</strong>.</p>
|
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<p><strong>Key findings:</strong></p>
|
||||
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<ul>
|
||||
<li>The LSTM autoencoder approach significantly <strong>outperforms Douglas-Peucker</strong> in terms of reconstruction accuracy, as measured by both <strong>discrete Fréchet distance</strong> and <strong>Dynamic Time Warping (DTW)</strong>.</li>
|
||||
<li>Unlike point-reduction techniques like Douglas-Peucker, our method performs a <strong>lossy reconstruction at every point</strong> along the trajectory. This offers potential advantages in maintaining temporal resolution and providing greater flexibility for downstream analysis.</li>
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<p>Experimental results demonstrate the effectiveness and potential benefits of using deep learning, specifically LSTM autoencoders, for GPS trajectory compression, offering improved accuracy over conventional geometric algorithms. <a class="citation" href="/publications#kolle2023compression">[Kölle et al. 2023]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2023-02-25T00:00:00+01:00">February 25, 2023</time></p>
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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="illium2020meantime"><span style="font-variant: small-caps">Illium, S., Friese, P.A., Müller, R., and Feld, S.</span> 2020. What to do in the meantime: A service coverage analysis for parked autonomous vehicles. <i>AGILE: GIScience Series</i> <i>1</i>, 7.</span>
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data-bibtex-json='"@article{illium2020meantime,\n title = {What to do in the meantime: A service coverage analysis for parked autonomous vehicles},\n author = {Illium, Steffen and Friese, Philipp Andreas and Müller, Robert and Feld, Sebastian},\n journal = {AGILE: GIScience Series},\n volume = {1},\n pages = {7},\n year = {2020},\n publisher = {Copernicus Publications Göttingen, Germany}\n}\n"'
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data-bibtex-json='"@article{illium2020meantime,\n title = {What to do in the meantime: A service coverage analysis for parked autonomous vehicles},\n author = {Illium, Steffen and Friese, Philipp Andreas and Müller, Robert and Feld, Sebastian},\n journal = {AGILE: GIScience Series},\n volume = {1},\n pages = {7},\n year = {2020},\n publisher = {Copernicus Publications Göttingen, Germany}\n}\n"'
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<center>
|
||||
<img src="/assets/figures/5_meantime_coverage.jpg" alt="Map visualization showing estimated service coverage areas from parked autonomous vehicles" style="display:block; width:80%" />
|
||||
<figcaption>Visualization of estimated service coverage achievable by utilizing parked autonomous vehicles.</figcaption>
|
||||
</center>
|
||||
<p><br /></p>
|
||||
|
||||
<p>This research investigates a potential transitional model towards future transportation systems, focusing on <strong>privately owned shared autonomous vehicles (SAVs)</strong>. The central idea, termed “What to do in the Meantime,” explores the feasibility of leveraging these vehicles for ride-sharing services during the significant portions of the day when they are typically parked and idle (e.g., while the owner is at work).</p>
|
||||
|
||||
<p>To assess the potential impact and viability of such a model, we developed and applied <strong>two distinct reachability analysis methods</strong>. These methods estimate the geographic area that could be effectively served by SAVs originating from their parking locations within given time constraints.</p>
|
||||
|
||||
<p>The analysis was conducted using a real-world dataset representing mobility patterns and parking durations in the greater <strong>Munich metropolitan area</strong>. Key findings reveal the significant influence of spatio-temporal factors on potential service coverage:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>Time Dependency:</strong> Service potential fluctuates considerably throughout the day, heavily impacted by rush hours which affect travel times and vehicle availability.</li>
|
||||
<li><strong>Location Dependency:</strong> Marked differences in coverage potential were observed between dense urban centers and more dispersed suburban areas.</li>
|
||||
</ul>
|
||||
|
||||
<p>This study provides quantitative insights into the opportunities and limitations of utilizing the “meantime” of privately owned autonomous vehicles, contributing to the understanding of how future shared mobility systems might evolve. <a class="citation" href="/publications#illium2020meantime">[Illium et al. 2020]</a></p>
|
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|
||||
<img src="/assets/figures/5_meantime_availability.jpg" alt="Graph or map showing the temporal or spatial availability of parked vehicles" style="display:block; width:80%" />
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<figcaption>Analysis of spatio-temporal availability patterns of potentially shareable parked vehicles.</figcaption>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2020-07-15T00:00:00+02:00">July 15, 2020</time></p>
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<ol class="bibliography"><li><span id="elsner2019deep"><span style="font-variant: small-caps">Elsner, D., Langer, S., Ritz, F., Mueller, R., and Illium, S.</span> 2019. Deep neural baselines for computational paralinguistics. <i>arXiv preprint arXiv:1907.02864</i>.</span>
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<p>This research, presented as part of the Interspeech 2019 Computational Paralinguistics Challenge (ComParE), specifically addresses the Sleepiness Sub-Challenge. We introduced a robust, end-to-end deep learning methodology designed to serve as a strong baseline for audio classification tasks within the paralinguistics domain.</p>
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<p>The core innovation lies in utilizing a deep neural network architecture (e.g., CNNs, potentially combined with recurrent layers) that directly processes raw or minimally processed audio data (such as spectrograms). This end-to-end approach bypasses the need for extensive, task-specific manual feature engineering, which is often a complex and time-consuming aspect of traditional audio analysis pipelines.</p>
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<p>Our proposed baseline model achieved performance comparable to established state-of-the-art methods on the sleepiness detection task. Furthermore, the architecture was designed with adaptability in mind, demonstrating its potential applicability to a broader range of audio classification challenges beyond sleepiness detection. This work underscores the power of deep learning to automatically extract relevant features from audio signals for complex paralinguistic tasks. For further details, please consult the publication by <a class="citation" href="/publications#elsner2019deep">[Elsner et al. 2019]</a>.</p>
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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="zorn23surprise"><span style="font-variant: small-caps">Zorn, M., Illium, S., Phan, T., Kaiser, T.K., Linnhoff-Popien, C., and Gabor, T.</span> 2023. Social Neural Network Soups with Surprise Minimization. MIT Press Direct.</span>
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<p>This research extends the study of <strong>artificial chemistry</strong> systems populated by neural network “particles,” focusing on the emergence of complex behaviors driven by <strong>social interaction</strong> rather than explicit programming. Building on systems where particles may exhibit self-replication, we introduce interactions based on principles of <strong>predictive processing and surprise minimization</strong> (akin to the Free Energy Principle).</p>
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\18_surprised_soup_schematic.jpg" alt="Schematic diagram illustrating interacting neural network particles in the 'social soup'" /></p>
|
||||
|
||||
<p>Specifically, particles are equipped with mechanisms enabling them to <strong>recognize and build predictive models of their peers’ behavior</strong>. The learning process is driven by the minimization of prediction error, or “surprise,” incentivizing particles to accurately anticipate the actions or state changes of others within the “soup.”</p>
|
||||
|
||||
<p>Key observations from this setup include:</p>
|
||||
<ul>
|
||||
<li>The emergence of <strong>stable behavioral patterns and population dynamics</strong> purely from these local, predictive interactions. Notably, these emergent patterns often resemble the stability observed in systems where self-replication was an explicitly trained objective.</li>
|
||||
<li>The introduction of a unique <strong>“catalyst” particle</strong> designed to exert evolutionary pressure on the system, demonstrating how external influences or specialized agents can shape the collective dynamics.</li>
|
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<img src="/assets/figures/18_surprised_soup_trajec.jpg" alt="Trajectories or state space visualization of the particle population dynamics over time" style="display:block; width:90%" />
|
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<figcaption>Visualization of particle trajectories or population dynamics within the 'social soup'.</figcaption>
|
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<p>This study highlights how complex, seemingly goal-directed social behaviors and stable ecosystem structures can emerge from simple, local rules based on mutual prediction and surprise minimization among interacting agents, offering insights into the self-organization of complex adaptive systems. <a class="citation" href="/publications#zorn23surprise">[Zorn et al. 2023]</a></p>
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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="gabor2022self"><span style="font-variant: small-caps">Gabor, T., Illium, S., Zorn, M., et al.</span> 2022. Self-replication in neural networks. <i>Artificial Life</i> <i>28</i>, 2, 205–223.</span>
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data-bibtex-json='"@article{gabor2022self,\n title = {Self-replication in neural networks},\n author = {Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia},\n journal = {Artificial Life},\n volume = {28},\n number = {2},\n pages = {205--223},\n year = {2022},\n publisher = {MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}\n}\n"'
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data-bibtex-json='"@article{gabor2022self,\n title = {Self-replication in neural networks},\n author = {Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia},\n journal = {Artificial Life},\n volume = {28},\n number = {2},\n pages = {205--223},\n year = {2022},\n publisher = {MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}\n}\n"'
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||||
<img src="/assets/figures/15_sr_journal_children.jpg" alt="Visualization showing the evolution or diversity of 'child' networks generated through self-replication" style="display:block; width:65%" />
|
||||
<figcaption>Analyzing the lineage and diversity in populations of self-replicating networks.</figcaption>
|
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|
||||
<p><br /></p>
|
||||
|
||||
<p>This journal article provides an extended and more in-depth exploration of self-replicating neural networks, building upon earlier foundational work (<a href="link-to-previous-paper-if-available">Gabor et al., 2019</a>). The research further investigates the use of <strong>backpropagation-like mechanisms</strong> not for typical supervised learning, but as an effective means to enable <strong>non-trivial self-replication</strong> – where networks learn to reproduce their own connection weights.</p>
|
||||
|
||||
<p>Key extensions and analyses presented in this work include:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>Robustness Analysis:</strong> A systematic evaluation of the self-replicating networks’ resilience and stability when subjected to various levels of <strong>noise</strong> during the replication process.</li>
|
||||
<li><strong>Artificial Chemistry Environments:</strong> Further development and analysis of simulated environments where populations of self-replicating networks interact, leading to observable <strong>emergent collective behaviors</strong> and ecosystem dynamics.</li>
|
||||
<li><strong>Dynamical Systems Perspective:</strong> A detailed theoretical analysis of the self-replication process viewed as a dynamical system. This includes identifying <strong>fixpoint weight configurations</strong> (networks that perfectly replicate themselves) and characterizing their <strong>attractor basins</strong> (the regions in weight space from which networks converge towards a specific fixpoint).</li>
|
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||||
<img src="/assets/figures/15_noise_levels.jpg" alt="Graph showing the impact of different noise levels on self-replication fidelity or population dynamics" style="display:block; width:65%" />
|
||||
<figcaption>Investigating the influence of noise on the self-replication process.</figcaption>
|
||||
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|
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<p><br /></p>
|
||||
|
||||
<p>By delving deeper into the mechanisms, robustness, emergent properties, and underlying dynamics, this study significantly enhances the understanding of how self-replication can be achieved and analyzed within neural network models, contributing valuable insights to the fields of artificial life and complex systems. <a class="citation" href="/publications#gabor2022self">[Gabor et al. 2022]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2022-08-01T00:00:00+02:00">August 1, 2022</time></p>
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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
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<ol class="bibliography"><li><span id="feld2018trajectory"><span style="font-variant: small-caps">Feld, S., Illium, S., Sedlmeier, A., and Belzner, L.</span> 2018. Trajectory annotation using sequences of spatial perception. <i>Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems</i>, 329–338.</span>
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<img src="/assets/figures/0_trajectory_isovist.jpg" alt="Visualization of spatial perception field (e.g., isovist) from a point on a trajectory" style="width:48%; display: inline-block; margin: 1%;" />
|
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<figcaption>Learning spatial context representations (left) enables clustering and annotation of trajectories (right).</figcaption>
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<p><br /></p>
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||||
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||||
<p>This research addresses the challenge of enabling more intuitive human-robot interaction in shared spaces, particularly focusing on grounding verbal communication in spatial understanding. The work introduces a novel unsupervised learning methodology based on neural autoencoders.</p>
|
||||
|
||||
<p>The core contribution is a system that learns continuous, low-dimensional representations of spatial context directly from trajectory data, without requiring explicit environmental maps or predefined regions. By processing sequences of spatial perceptions (analogous to visibility fields or isovists) along a path, the autoencoder captures salient environmental features relevant to movement.</p>
|
||||
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||||
<p>These learned latent representations facilitate the effective clustering of trajectories based on shared spatial experiences. The outcome is a set of semantically meaningful encodings and prototypical representations of movement patterns within an environment. This approach lays essential groundwork for developing robotic systems capable of understanding, interpreting, and potentially describing movement through space in human-comprehensible terms, representing a promising direction for future human-robot collaboration. <a class="citation" href="/publications#feld2018trajectory">[Feld et al. 2018]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2018-11-01T00:00:00+01:00">November 1, 2018</time></p>
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<ol class="bibliography"><li><span id="altmann2024emergence"><span style="font-variant: small-caps">Altmann, P., Schönberger, J., Illium, S., et al.</span> 2024. Emergence in Multi-agent Systems: A Safety Perspective. <i>International Symposium on Leveraging Applications of Formal Methods</i>, Springer Nature Switzerland Cham, 104–120.</span>
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data-bibtex-json='"@inproceedings{altmann2024emergence,\n title = {Emergence in Multi-agent Systems: A Safety Perspective},\n author = {Altmann, Philipp and Schönberger, Julian and Illium, Steffen and Zorn, Maximilian and Ritz, Fabian and Haider, Tom and Burton, Simon and Gabor, Thomas},\n booktitle = {International Symposium on Leveraging Applications of Formal Methods},\n pages = {104--120},\n year = {2024},\n organization = {Springer Nature Switzerland Cham}\n}\n"'
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data-umami-event-bibkey="altmann2024emergence"
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data-umami-event-slug="mas-emergence-safety"
|
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data-bibtex-json='"@inproceedings{altmann2024emergence,\n title = {Emergence in Multi-agent Systems: A Safety Perspective},\n author = {Altmann, Philipp and Schönberger, Julian and Illium, Steffen and Zorn, Maximilian and Ritz, Fabian and Haider, Tom and Burton, Simon and Gabor, Thomas},\n booktitle = {International Symposium on Leveraging Applications of Formal Methods},\n pages = {104--120},\n year = {2024},\n organization = {Springer Nature Switzerland Cham}\n}\n"'
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\21_envs.png" alt="Diagrams of the gridworld environments used for evaluation" /></p>
|
||||
|
||||
<p>Multi-Agent Systems (MAS), particularly those employing decentralized decision-making based on local information (common in MARL), can exhibit <strong>emergent effects</strong>. These phenomena, arising from complex interactions, range from minor behavioral quirks to potentially catastrophic system failures, posing significant <strong>safety challenges</strong>.</p>
|
||||
|
||||
<p>This research provides a framework for understanding and mitigating undesirable emergence from a <strong>safety perspective</strong>. We propose a formal definition: emergent effects arise from <strong>misalignments between the <em>global inherent specification</em></strong> (the intended overall system goal or behavior) <strong>and its <em>local approximation</em></strong> used by individual agents (e.g., distinct reward components, limited observations).</p>
|
||||
|
||||
<center>
|
||||
<img src="/assets/figures/21_coins.png" alt="Visualization showing agents exhibiting emergent coin-collecting behavior" style="display:block; width:70%" />
|
||||
<figcaption>Example of emergent behavior (e.g., coin hoarding) due to specification misalignment.</figcaption>
|
||||
</center>
|
||||
<p><br /></p>
|
||||
|
||||
<p>Leveraging established concepts from system safety engineering, we analyze how such misalignments can lead to deviations from intended global behavior. To illustrate the practical implications, we examine two highly configurable gridworld scenarios. These demonstrate how inadequate or independently derived local specifications (rewards/observations) can predictably result in unintended emergent behaviors, such as resource hoarding or inefficient coordination.</p>
|
||||
|
||||
<center>
|
||||
<img src="/assets/figures/21_blocking.png" alt="Visualization showing agents exhibiting emergent blocking behavior" style="display:block; width:60%" />
|
||||
<figcaption>Example of emergent behavior (e.g., mutual blocking) due to specification misalignment.</figcaption>
|
||||
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|
||||
<p><br /></p>
|
||||
|
||||
<p>Recognizing that achieving a perfectly aligned global specification might be impractical in complex systems, we propose strategies focused on <strong>adjusting the underlying local parameterizations</strong> (e.g., reward shaping, observation design) to mitigate harmful emergence. By carefully tuning these local components, system alignment can be improved, reducing the risk of emergent failures and enhancing overall safety. <a class="citation" href="/publications#altmann2024emergence">[Altmann et al. 2024]</a></p>
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<ol class="bibliography"><li><span id="gabor2019self"><span style="font-variant: small-caps">Gabor, T., Illium, S., Mattausch, A., Belzner, L., and Linnhoff-Popien, C.</span> 2019. <i>Self-Replication in Neural Networks</i>. .</span>
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<p>Drawing inspiration from the fundamental process of self-replication in biological systems, this research explores the potential for implementing analogous mechanisms within neural networks. The objective is to develop computational models capable of autonomously reproducing their own structure (specifically, their connection weights), potentially leading to the emergence of complex, adaptive behaviors.</p>
|
||||
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||||
<p>The study investigates various neural network architectures and learning paradigms suitable for achieving self-replication. A key finding highlights the efficacy of leveraging backpropagation-like mechanisms, not for a typical supervised task, but for navigating the weight space in a manner conducive to replication. This approach facilitates the development of non-trivial self-replicating networks.</p>
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<p>Furthermore, the research extends this concept by proposing an “artificial chemistry” environment. This framework involves populations of interacting neural networks, where self-replication dynamics can lead to emergent properties and complex ecosystem behaviors. This work offers a novel computational perspective on self-replication, providing tools and insights for exploring artificial life and the principles of self-organization in computational systems. For a detailed discussion, please refer to the publication by <a class="citation" href="/publications#gabor2019self">[Gabor et al. 2019]</a>.</p>
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<figcaption>Visualization of self-replicator populations evolving in a PCA-reduced weight space.</figcaption>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2019-07-01T00:00:00+02:00">July 1, 2019</time></p>
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<ol class="bibliography"><li><span id="illium2022constructing"><span style="font-variant: small-caps">Illium, S., Zorn, M., Lenta, C., Kölle, M., Linnhoff-Popien, C., and Gabor, T.</span> 2022. Constructing organism networks from collaborative self-replicators. <i>2022 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, IEEE, 1268–1275.</span>
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data-bibtex-json='"@inproceedings{illium2022constructing,\n title = {Constructing organism networks from collaborative self-replicators},\n author = {Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and K{\\\"o}lle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},\n booktitle = {2022 IEEE Symposium Series on Computational Intelligence (SSCI)},\n pages = {1268--1275},\n year = {2022},\n organization = {IEEE}\n}\n"'
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data-bibtex-json='"@inproceedings{illium2022constructing,\n title = {Constructing organism networks from collaborative self-replicators},\n author = {Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and K{\\\"o}lle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},\n booktitle = {2022 IEEE Symposium Series on Computational Intelligence (SSCI)},\n pages = {1268--1275},\n year = {2022},\n organization = {IEEE}\n}\n"'
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<p>This research investigates the transition from simple self-replication to higher levels of organization by exploring how populations of basic, self-replicating neural network units can form <strong>“Organism Networks” (ONs)</strong> through <strong>collaboration and emergent differentiation</strong>. Moving beyond the replication of individual networks, the focus shifts to the collective dynamics and functional capabilities that arise when these units interact within a shared environment (akin to an “artificial chemistry”).</p>
|
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<center>
|
||||
<img src="/assets/figures/16_on_architecture.jpg" alt="Diagram showing individual self-replicating units interacting to form a larger Organism Network structure" style="display:block; width:65%" />
|
||||
<figcaption>Conceptual architecture of an Organism Network emerging from interacting self-replicators.</figcaption>
|
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|
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<p><br /></p>
|
||||
|
||||
<p>The core hypothesis is that through local interactions and potentially shared environmental feedback, initially homogeneous populations of self-replicators can spontaneously develop specialized roles or structures, leading to a collective entity with capabilities exceeding those of individual units.</p>
|
||||
|
||||
<p style="display:block; width:45%" class="align-right"><img src="\assets\figures\16_on_dropout.jpg" alt="Visualization potentially related to network robustness, differentiation, or communication channels." /></p>
|
||||
|
||||
<p>Key aspects explored in this work include:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>Mechanisms for Collaboration:</strong> Investigating how communication or resource sharing between self-replicating units can be established and influence collective behavior.</li>
|
||||
<li><strong>Emergent Differentiation:</strong> Analyzing scenarios where units within the population begin to specialize, adopting different internal states (weight configurations) or functions, analogous to cellular differentiation in biological organisms.</li>
|
||||
<li><strong>Formation of Structure:</strong> Studying how interactions lead to stable spatial or functional structures within the population, forming the basis of the Organism Network.</li>
|
||||
<li><strong>Functional Advantages:</strong> Assessing whether these emergent ONs exhibit novel collective functionalities or improved problem-solving capabilities compared to non-interacting populations. (The role of dropout, as suggested by the image, might relate to promoting robustness or specialization within this context).</li>
|
||||
</ul>
|
||||
|
||||
<p>This study bridges the gap between single-unit self-replication and the emergence of complex, multi-unit systems in artificial life research, offering insights into how collaborative dynamics can lead to higher-order computational structures. For more detailed insights, refer to <a class="citation" href="/publications#illium2022constructing">[Illium et al. 2022]</a>.</p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2022-12-01T00:00:00+01:00">December 1, 2022</time></p>
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<ol class="bibliography"><li><span id="sedlmeier2020policy"><span style="font-variant: small-caps">Sedlmeier, A., Müller, R., Illium, S., and Linnhoff-Popien, C.</span> 2020. Policy entropy for out-of-distribution classification. <i>Artificial Neural Networks and Machine Learning–ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II 29</i>, Springer International Publishing, 420–431.</span>
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data-bibtex-json='"@inproceedings{sedlmeier2020policy,\n title = {Policy entropy for out-of-distribution classification},\n author = {Sedlmeier, Andreas and Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},\n booktitle = {Artificial Neural Networks and Machine Learning--ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15--18, 2020, Proceedings, Part II 29},\n pages = {420--431},\n year = {2020},\n organization = {Springer International Publishing}\n}\n"'
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data-bibtex-json='"@inproceedings{sedlmeier2020policy,\n title = {Policy entropy for out-of-distribution classification},\n author = {Sedlmeier, Andreas and Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},\n booktitle = {Artificial Neural Networks and Machine Learning--ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15--18, 2020, Proceedings, Part II 29},\n pages = {420--431},\n year = {2020},\n organization = {Springer International Publishing}\n}\n"'
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<p style="display:block; width:45%" class="align-right"><img src="\assets\figures\6_ood_performance.jpg" alt="Graph comparing PEOC performance against other OOD detection methods" /></p>
|
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||||
<p>Ensuring the safety and reliability of deep reinforcement learning (RL) agents deployed in real-world environments necessitates the ability to detect when the agent encounters states significantly different from those seen during training (i.e., out-of-distribution or OOD states). This research introduces <strong>PEOC (Policy Entropy-based OOD Classifier)</strong>, a novel and computationally efficient method designed for this purpose.</p>
|
||||
|
||||
<p>The core idea behind PEOC is to leverage the entropy of the agent’s learned policy as an intrinsic indicator of state familiarity. High policy entropy often correlates with uncertainty, suggesting the agent is in a less familiar or potentially OOD state. PEOC utilizes this readily available metric as a scoring function to distinguish between in-distribution and out-of-distribution inputs.</p>
|
||||
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||||
<p>PEOC’s effectiveness was rigorously evaluated within procedurally generated environments, which allow for controlled introduction of novel states. Its performance was benchmarked against several state-of-the-art one-class classification methods adapted for the RL context. The results demonstrate that PEOC achieves competitive performance in identifying OOD states while being simple to implement and integrate into existing deep RL frameworks.</p>
|
||||
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||||
<p>Furthermore, this work contributes a structured benchmarking process specifically designed for evaluating OOD classification methods within the context of reinforcement learning, providing a valuable framework for assessing the reliability of such safety-critical components. For a detailed methodology and evaluation, please refer to the publication by <a class="citation" href="/publications#sedlmeier2020policy">[Sedlmeier et al. 2020]</a>.</p>
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<p>Conceptual pipeline of the PEOC method for OOD detection in deep RL.</figcaption>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2020-06-01T00:00:00+02:00">June 1, 2020</time></p>
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<ol class="bibliography"><li><span id="koelle23primate"><span style="font-variant: small-caps">Kölle, M., Illium, S., Zorn, M., Nüßlein, J., Suchostawski, P., and Linnhoff-Popien, C.</span> 2023. Improving Primate Sounds Classification using Binary Presorting for Deep Learning. Springer CCIS Series.</span>
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data-bibtex-json='"@inproceedings{koelle23primate,\n title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning},\n author = {Kölle, Michael and Illium, Steffen and Zorn, Maximilian and Nü{\\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia},\n year = {2023},\n organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023},\n publisher = {Springer CCIS Series}\n}\n"'
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data-bibtex-json='"@inproceedings{koelle23primate,\n title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning},\n author = {Kölle, Michael and Illium, Steffen and Zorn, Maximilian and Nü{\\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia},\n year = {2023},\n organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023},\n publisher = {Springer CCIS Series}\n}\n"'
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\19_binary_primates_pipeline.jpg" alt="Diagram illustrating the multi-class training pipeline incorporating subsegment relabeling" /></p>
|
||||
|
||||
<p>Automated acoustic classification plays a vital role in wildlife monitoring and bioacoustics research. This study introduces a sophisticated pre-processing and training strategy to significantly enhance the accuracy of multi-class audio classification, specifically targeting the identification of different primate species from field recordings.</p>
|
||||
|
||||
<p>A key challenge in bioacoustics is dealing with datasets containing weak labels (where calls of interest occupy only a portion of a labeled segment), varying segment lengths, and poor signal-to-noise ratios (SNR). Our approach addresses this by:</p>
|
||||
|
||||
<ol>
|
||||
<li><strong>Subsegment Analysis:</strong> Processing audio recordings represented as <strong>MEL spectrograms</strong>.</li>
|
||||
<li><strong>Refined Labeling:</strong> Meticulously <strong>relabeling subsegments</strong> within the spectrograms. This “binary presorting” step effectively identifies and isolates the actual vocalizations of interest within longer, weakly labeled recordings.</li>
|
||||
<li><strong>CNN Training:</strong> Training <strong>Convolutional Neural Networks (CNNs)</strong> on these refined, higher-quality subsegment inputs.</li>
|
||||
<li><strong>Data Augmentation:</strong> Employing innovative <strong>data augmentation techniques</strong> suitable for spectrogram data to further improve model robustness.</li>
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<center>
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<img src="/assets/figures/19_binary_primates_thresholding.jpg" alt="Visualization related to the thresholding or selection process for subsegment labeling" style="display:block; width:70%" />
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<figcaption>Thresholding or selection criteria for subsegment refinement.</figcaption>
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<p><br /></p>
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||||
<p>The effectiveness of this methodology was evaluated on the challenging <strong>ComParE 2021 Primate dataset</strong>. The results demonstrate remarkable improvements in classification performance, achieving substantially higher accuracy and Unweighted Average Recall (UAR) scores compared to existing baseline methods.</p>
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<figcaption>Comparative performance results on the ComParE 2021 dataset.</figcaption>
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<p><br /></p>
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<p>This work represents a significant advancement in handling difficult, real-world bioacoustic data, showcasing how careful data refinement prior to deep learning model training can dramatically enhance classification outcomes. <a class="citation" href="/publications#koelle23primate">[Kölle et al. 2023]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2023-06-25T00:00:00+02:00">June 25, 2023</time></p>
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<ol class="bibliography"><li><span id="muller2021deep"><span style="font-variant: small-caps">Müller, R., Illium, S., and Linnhoff-Popien, C.</span> 2021. A Deep and Recurrent Architecture for Primate Vocalization Classification. <i>Proc. Interspeech 2021</i>, 461–465.</span>
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data-bibtex-json='"@article{muller2021deep,\n title = {A Deep and Recurrent Architecture for Primate Vocalization Classification},\n author = {Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},\n journal = {Proc. Interspeech 2021},\n pages = {461--465},\n year = {2021}\n}\n"'
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<p style="display:block; width:40%" class="align-right"><img src="\assets\figures\11_recurrent_primate_workflow.jpg" alt="Workflow diagram showing audio input, feature extraction, BiLSTM processing, and classification output" /></p>
|
||||
|
||||
<p>Acoustic monitoring offers a powerful, non-invasive tool for wildlife conservation, enabling the study and tracking of animal populations through their vocalizations. This research focuses on improving the automated classification of <strong>primate vocalizations</strong>, a challenging task due to call variability and environmental noise.</p>
|
||||
|
||||
<p>We propose a novel <strong>deep, recurrent neural network architecture</strong> specifically designed for this purpose. The core of the model utilizes <strong>bidirectional Long Short-Term Memory (BiLSTM) networks</strong>, which are adept at capturing temporal dependencies within the audio signals (represented, for example, as spectrograms or MFCCs).</p>
|
||||
|
||||
<p>To further enhance classification performance, particularly in potentially imbalanced datasets common in bioacoustics, the architecture incorporates advanced techniques:</p>
|
||||
<ul>
|
||||
<li><strong>Normalized Softmax:</strong> Improves calibration and potentially robustness.</li>
|
||||
<li><strong>Focal Loss:</strong> Addresses class imbalance by focusing training on hard-to-classify examples.</li>
|
||||
</ul>
|
||||
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<p>Hyperparameter tuning, a critical step for optimizing deep learning models, was systematically performed using <strong>Bayesian optimization</strong>.</p>
|
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|
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<figcaption>Performance results demonstrating classification accuracy.</figcaption>
|
||||
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|
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<p><br /></p>
|
||||
|
||||
<p>The model’s effectiveness was evaluated on a challenging real-world dataset comprising diverse primate calls recorded at an <strong>African wildlife sanctuary</strong>. The results demonstrate the capability of the proposed deep recurrent architecture for accurate primate vocalization classification, underscoring the potential of advanced deep learning techniques combined with automated acoustic monitoring for practical wildlife conservation efforts. <a class="citation" href="/publications#muller2021deep">[Müller et al. 2021]</a></p>
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<ol class="bibliography"><li><span id="muller2022towards"><span style="font-variant: small-caps">Müller, R., Illium, S., Phan, T., Haider, T., and Linnhoff-Popien, C.</span> 2022. Towards Anomaly Detection in Reinforcement Learning. <i>Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems</i>, 1799–1803.</span>
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<p>Anomaly Detection (AD) is crucial for the safe deployment of Reinforcement Learning (RL) agents, especially in safety-critical applications where encountering unexpected or out-of-distribution situations can lead to catastrophic failures. This work provides a perspective on the state and future directions of AD research specifically tailored for the complexities inherent in RL.</p>
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<p>The paper argues that current AD research within RL often relies on overly simplified scenarios that do not fully capture the challenges of sequential decision-making under uncertainty. It establishes important conceptual connections between AD and other critical areas of RL research:</p>
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<li><strong>Lifelong Reinforcement Learning:</strong> AD is framed as a necessary component for agents that must continually adapt to changing environments and tasks. Detecting anomalies signals the need for adaptation or learning updates.</li>
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<li><strong>Generalization:</strong> The ability to detect anomalies is closely related to an agent’s generalization capabilities; anomalies often represent situations outside the agent’s learned experience manifold.</li>
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<p>The study highlights <strong>non-stationarity</strong> (i.e., changes in the environment dynamics or reward structure over time) as a particularly critical and under-explored challenge for AD in RL. To address this formally, the paper proposes utilizing the framework of <strong>block contextual Markov decision processes (BCMDPs)</strong> as a suitable model for defining and analyzing non-stationary anomalies.</p>
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<figcaption>Formalizing non-stationary anomalies using the BCMDP framework.</figcaption>
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<p>Finally, it outlines practical requirements and desiderata for future research in this area, advocating for more rigorous evaluation protocols and benchmark environments to advance the development of robust and reliable AD methods for RL agents. <a class="citation" href="/publications#muller2022towards">[Müller et al. 2022]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2022-05-09T00:00:00+02:00">May 9, 2022</time></p>
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<ol class="bibliography"><li><span id="illium2022empirical"><span style="font-variant: small-caps">Illium, S., Schillman, T., Müller, R., Gabor, T., and Linnhoff-Popien, C.</span> 2022. Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks. <i>14th International Conference on Agents and Artificial Intelligence: ICAART</i>, 308–315.</span>
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data-bibtex-json='"@inproceedings{illium2022empirical,\n title = {Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks},\n author = {Illium, Steffen and Schillman, Thore and Müller, Robert and Gabor, Thomas and Linnhoff-Popien, Claudia},\n booktitle = {14th International Conference on Agents and Artificial Intelligence: ICAART},\n volume = {3},\n number = {Proceedings},\n pages = {308--315},\n year = {2022}\n}\n"'
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<p>Recurrent Neural Networks (RNNs), including variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are designed with the intent to capture temporal dependencies within sequential data. Their internal mechanisms allow information from previous time steps to influence current processing.</p>
|
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||||
<p>This research investigates the fundamental memory capacity of these architectures under challenging conditions: specifically, when processing sequences where data points are generated independently, possessing <strong>no inherent temporal correlation</strong>. In such scenarios, any recall of past inputs relies solely on the network’s ability to explicitly memorize information through standard backpropagation training, rather than leveraging statistical patterns in the sequence.</p>
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<p>Our empirical analysis demonstrates that while RNNs <em>can</em> learn to recall a limited number of past inputs even from uncorrelated sequences, this capability is significantly constrained:</p>
|
||||
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||||
<ul>
|
||||
<li><strong>Limited Recall Range:</strong> The effective range over which vanilla RNNs, LSTMs, and GRUs can reliably reproduce past inputs from uncorrelated data is substantially shorter than the recall range achievable when even minimal temporal correlations are present.</li>
|
||||
<li><strong>Architectural Influence:</strong> This limitation is influenced by both the specific RNN architecture (vanilla, LSTM, GRU) and the network size (number of hidden units).</li>
|
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<li><strong>Practical Bound:</strong> The findings suggest a practical upper bound on the temporal memory achievable through standard training in these scenarios, which appears well below theoretical information storage limits.</li>
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<img src="/assets/figures/22_rnn_limits.png" alt="Clearly visible memory horizon, established by emperical analysis of an atomic experriment." style="display:block; width:100%" />
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<figcaption>RNN Memory Horizon.</figcaption>
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<p>These results highlight an inherent constraint in the capacity of standard RNN architectures to identify and utilize long-range dependencies when processing sequences lacking temporal structure, providing insights into their limitations in specific types of sequence modeling tasks. <a class="citation" href="/publications#illium2022empirical">[Illium et al. 2022]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2022-02-25T00:00:00+01:00">February 25, 2022</time></p>
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<meta itemprop="description" content="STEVE learns soccer team embeddings from match data for analysis.">
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<a href="http://localhost:4000/research/soccer-team-vectors/" itemprop="url">Soccer Team Vectors
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<ol class="bibliography"><li><span id="muller2020soccer"><span style="font-variant: small-caps">Müller, R., Langer, S., Ritz, F., Roch, C., Illium, S., and Linnhoff-Popien, C.</span> 2020. Soccer Team Vectors. <i>Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II</i>, Springer International Publishing, 247–257.</span>
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data-bibtex-json='"@inproceedings{muller2020soccer,\n title = {Soccer Team Vectors},\n author = {Müller, Robert and Langer, Stefan and Ritz, Fabian and Roch, Christoph and Illium, Steffen and Linnhoff-Popien, Claudia},\n booktitle = {Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16--20, 2019, Proceedings, Part II},\n pages = {247--257},\n year = {2020},\n organization = {Springer International Publishing}\n}\n"'
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data-bibtex-json='"@inproceedings{muller2020soccer,\n title = {Soccer Team Vectors},\n author = {Müller, Robert and Langer, Stefan and Ritz, Fabian and Roch, Christoph and Illium, Steffen and Linnhoff-Popien, Claudia},\n booktitle = {Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16--20, 2019, Proceedings, Part II},\n pages = {247--257},\n year = {2020},\n organization = {Springer International Publishing}\n}\n"'
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<p>This research introduces <strong>STEVE (Soccer Team Vectors)</strong>, a novel methodology for learning meaningful, real-valued vector representations (embeddings) for professional soccer teams. The primary goal is to capture intrinsic team characteristics and relationships within a continuous vector space, such that teams with similar playing styles, strengths, or performance levels are positioned closely together.</p>
|
||||
|
||||
<p>Leveraging widely available public data from soccer matches (e.g., results, possibly performance statistics), STEVE employs machine learning techniques to generate these low-dimensional team vectors.</p>
|
||||
|
||||
<p>The utility of these learned representations is demonstrated through several downstream applications:</p>
|
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<p><img src="\assets\figures\2_steve_algo.jpg" alt="alt text" style="display:block; width:60%" class="align-right" /></p>
|
||||
|
||||
<ul>
|
||||
<li><strong>Team Market Value Estimation:</strong> The vectors serve as effective features for predicting team market values, outperforming baseline models.</li>
|
||||
<li><strong>Similarity Search:</strong> The vector space allows for efficient identification of teams similar to a given query team based on proximity.</li>
|
||||
<li><strong>Team Ranking:</strong> The embeddings provide a basis for generating data-driven team rankings.</li>
|
||||
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|
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||||
<p>Across these application domains, STEVE demonstrated superior performance compared to competing approaches evaluated in the study. This work provides a valuable tool for quantitative analysis in sports analytics, enabling various machine learning tasks related to team comparison and prediction. For a comprehensive description of the methodology and results, please refer to the publication by <a class="citation" href="/publications#muller2020soccer">[Müller et al. 2020]</a>.</p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2020-03-01T00:00:00+01:00">March 1, 2020</time></p>
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<ol class="bibliography"><li><span id="muller2020acoustic"><span style="font-variant: small-caps">Müller, R., Ritz, F., Illium, S., and Linnhoff-Popien, C.</span> 2020. Acoustic anomaly detection for machine sounds based on image transfer learning. <i>arXiv preprint arXiv:2006.03429</i>.</span>
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<p style="display:block; width:45%" class="align-right"><img src="\assets\figures\9_image_transfer_sound_workflow.jpg" alt="Workflow diagram showing mel-spectrogram input, feature extraction via image network, and anomaly detection model" /></p>
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||||
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||||
<p>This study investigates an effective approach for <strong>acoustic anomaly detection</strong> in industrial machinery, focusing on identifying malfunctions through sound analysis. The core methodology leverages <strong>transfer learning</strong> by repurposing deep neural networks originally trained for large-scale <strong>image classification</strong> (e.g., on ImageNet) as powerful feature extractors for audio data represented as <strong>mel-spectrograms</strong>.</p>
|
||||
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||||
<p>The process involves:</p>
|
||||
<ol>
|
||||
<li>Converting audio signals from machinery into mel-spectrogram images.</li>
|
||||
<li>Feeding these spectrograms into various pretrained image classification networks (specifically comparing <strong>ResNet architectures</strong> against <strong>AlexNet</strong> and <strong>SqueezeNet</strong>) to extract deep feature representations.</li>
|
||||
<li>Training standard anomaly detection models – particularly <strong>Gaussian Mixture Models (GMMs)</strong> and <strong>One-Class Support Vector Machines (OC-SVMs)</strong> – on the features extracted from normal operation sounds.</li>
|
||||
<li>Classifying new sounds as anomalous if their extracted features deviate significantly from the learned normality model.</li>
|
||||
</ol>
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||||
|
||||
<p>Key findings from the experiments, conducted across different machine types and noise conditions, include:</p>
|
||||
|
||||
<ul>
|
||||
<li>The proposed transfer learning approach significantly <strong>outperforms baseline methods like traditional convolutional autoencoders</strong>, especially in the presence of background noise.</li>
|
||||
<li>Features extracted using <strong>ResNet architectures consistently yielded superior anomaly detection performance</strong> compared to those from AlexNet and SqueezeNet.</li>
|
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<li><strong>GMMs and OC-SVMs proved highly effective</strong> as anomaly detection classifiers when applied to these transferred features.</li>
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<img src="/assets/figures/9_image_transfer_sound_mels.jpg" alt="Examples of mel-spectrograms from normal and anomalous machine sounds" style="display:block; width:85%" />
|
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<figcaption>Mel-spectrogram examples of normal vs. anomalous machine sounds.</figcaption>
|
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</center>
|
||||
|
||||
<p>This work demonstrates the surprising effectiveness of transferring knowledge from the visual domain to the acoustic domain for anomaly detection, offering a robust and readily implementable method for monitoring industrial equipment. <a class="citation" href="/publications#muller2020acoustic">[Müller et al. 2020]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-02T00:00:00+01:00">March 2, 2021</time></p>
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<ol class="bibliography"><li><span id="illium2020surgical"><span style="font-variant: small-caps">Illium, S., Müller, R., Sedlmeier, A., and Linnhoff-Popien, C.</span> 2020. Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms. <i>Proc. Interspeech 2020</i>, 2052–2056.</span>
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data-bibtex-json='"@inproceedings{illium2020surgical,\n title = {Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms},\n author = {Illium, Steffen and M{\\\"u}ller, Robert and Sedlmeier, Andreas and Linnhoff-Popien, Claudia},\n booktitle = {Proc. Interspeech 2020},\n pages = {2052--2056},\n year = {2020}\n}\n"'
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data-bibtex-json='"@inproceedings{illium2020surgical,\n title = {Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms},\n author = {Illium, Steffen and M{\\\"u}ller, Robert and Sedlmeier, Andreas and Linnhoff-Popien, Claudia},\n booktitle = {Proc. Interspeech 2020},\n pages = {2052--2056},\n year = {2020}\n}\n"'
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<p>This study investigates the efficacy of various <strong>data augmentation techniques</strong> applied directly to <strong>mel-spectrogram representations</strong> of audio data for improving classification performance. The specific task addressed is the detection of surgical mask usage based on human speech signals, a relevant problem in paralinguistics and audio analysis.</p>
|
||||
|
||||
<p>We systematically evaluated the impact of data augmentation when training <strong>Convolutional Neural Networks (CNNs)</strong> for this binary classification task. The input to the networks consisted of mel-spectrograms derived from voice samples. The effectiveness of augmentation strategies (such as frequency masking, time masking, or combined approaches like SpecAugment) was assessed across <strong>four different CNN architectures</strong>.</p>
|
||||
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|
||||
<img src="/assets/figures/7_mask_mels.jpg" alt="Examples of mel-spectrograms of speech with and without a surgical mask" style="display:block; width:80%" />
|
||||
<figcaption>Mel-spectrogram representations of speech signals used as input for CNNs.</figcaption>
|
||||
</center>
|
||||
<p><br /></p>
|
||||
|
||||
<p>The core finding of this research is that applying appropriate data augmentation directly to the spectrogram inputs significantly enhances the performance and generalization capabilities of the CNN models for surgical mask detection. The augmented models demonstrated improved accuracy, robustness, and notably <strong>surpassed many established benchmark results</strong> from the relevant ComParE (Computational Paralinguistics Challenge) tasks. This highlights the importance of data augmentation as a crucial component in building effective deep learning models for audio classification, particularly when dealing with limited or variable datasets. For a detailed description of the methods and results, please refer to <a class="citation" href="/publications#illium2020surgical">[Illium et al. 2020]</a>.</p>
|
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|
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<img src="/assets/figures/7_mask_models.jpg" alt="Diagrams illustrating the different CNN architectures tested" style="display:block; width:100%" />
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<figcaption>Overview of the different Convolutional Neural Network architectures evaluated.</figcaption>
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<ol class="bibliography"><li><span id="gabor2021goals"><span style="font-variant: small-caps">Gabor, T., Illium, S., Zorn, M., and Linnhoff-Popien, C.</span> 2021. Goals for self-replicating neural networks. <i>Artificial Life Conference Proceedings 33</i>, MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …, 101.</span>
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<p>Building upon the concept of self-replicating neural networks, this research explores the integration of <strong>auxiliary functional goals</strong> alongside the primary objective of self-replication. The aim is to create networks that can not only reproduce their own weights but also perform useful computations or interact meaningfully with an environment simultaneously.</p>
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<img src="/assets/figures/13_sr_analysis.jpg" alt="Analysis graphs or visualizations related to dual-task self-replicating networks" style="display:block; width:80%" />
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<figcaption>Analysis of networks balancing self-replication and auxiliary tasks.</figcaption>
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<p><br /></p>
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<p>The study introduces a methodology for <strong>dual-task training</strong>, utilizing distinct input/output vectors to manage both the replication process and the execution of a secondary task. A key finding is that the presence of an auxiliary task does not necessarily hinder self-replication; instead, it can sometimes <strong>complement and even stabilize</strong> the replication dynamics.</p>
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<p>Further investigations were conducted within the framework of an <strong>“artificial chemistry” environment</strong>, where populations of these dual-task networks interact:</p>
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||||
<ul>
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||||
<li>The impact of varying <strong>action parameters</strong> (related to the secondary task) on the collective learning or emergent behavior of the network population was examined.</li>
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<li>A concept of a specially designed <strong>“guiding particle”</strong> network was introduced. This network influences its peers, demonstrating a mechanism for potentially steering the population’s evolution towards desired goal-oriented behaviors.</li>
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<p>This work provides insights into how functional complexity can be integrated with self-replication in computational systems, offering potential pathways for developing more sophisticated artificial life models and exploring guided evolution within network populations. <a class="citation" href="/publications#gabor2021goals">[Gabor et al. 2021]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-06T00:00:00+01:00">March 6, 2021</time></p>
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<span id="illium2023voronoipatches"><span style="font-variant: small-caps">Illium, S., Griffin, G., Kölle, M., Zorn, M., Nüsslein, J., and Linnhoff-Popien, C.</span> 2023. VoronoiPatches: Evaluating a New Data Augmentation Method. <i>International Conference on Agents and Artificial Intelligence</i>, 350–357.</span>
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<p>Data augmentation is essential for improving the performance and generalization of Convolutional Neural Networks (CNNs), especially when training data is limited. This research introduces <strong>VoronoiPatches (VP)</strong>, a novel data augmentation algorithm based on the principle of <strong>non-linear recombination</strong> of image information.</p>
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<center>
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<img src="/assets/figures/17_vp_lion.jpg" alt="Example of an image augmented with VoronoiPatches, showing polygon patches blended onto a lion image" style="display:block; width:85%">
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<figcaption>Visual example of the VoronoiPatches augmentation applied to an image.</figcaption>
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<p><br></p>
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||||
<p>Unlike traditional methods that often apply uniform transformations or cutout regions, VP operates by:</p>
|
||||
<ol>
|
||||
<li>Generating a random layout of points within an image.</li>
|
||||
<li>Creating a Voronoi diagram based on these points, partitioning the image into unique, convex polygon-shaped patches.</li>
|
||||
<li>Redistributing information between these patches or blending information across patch boundaries (specific mechanism detailed in the paper).</li>
|
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<p>This approach potentially allows for smoother transitions between augmented regions and the original image compared to sharp cutout methods. The core idea is to encourage the CNN to learn more robust features by exposing it to varied, non-linearly recombined versions of the input data.</p>
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<p><img class="emoji" title=":trophy:" alt=":trophy:" src="https://github.githubassets.com/images/icons/emoji/unicode/1f3c6.png" height="20" width="20"> Best Poster Award - ICAART 2023 <img class="emoji" title=":trophy:" alt=":trophy:" src="https://github.githubassets.com/images/icons/emoji/unicode/1f3c6.png" height="20" width="20"><br>
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<p>Evaluations demonstrate that VoronoiPatches can effectively <strong>reduce model variance and combat overfitting</strong>. Comparative studies indicate that VP <strong>outperforms several existing state-of-the-art data augmentation techniques</strong> in improving the robustness and generalization performance of CNN models on unseen data across various benchmarks. <a class="citation" href="/publications#illium2023voronoipatches">[Illium et al. 2023]</a></p>
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<figcaption>Comparative results illustrating the performance benefits of VoronoiPatches.</figcaption>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2023-02-24T00:00:00+01:00">February 24, 2023</time></p>
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||||
|
||||
|
||||
|
||||
<div class="list__item">
|
||||
<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork" style="display: flex; align-items: flex-start;">
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||||
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||||
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||||
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||||
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|
||||
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|
||||
<img src="/assets/figures/21_coins_teaser.png" alt="" style="display: block;">
|
||||
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|
||||
|
||||
|
||||
|
||||
<div style="flex-grow: 1;">
|
||||
<h2 class="archive__item-title no_toc" itemprop="headline" style="text-align: left !important;">
|
||||
|
||||
<a href="/research/mas-emergence-safety/" rel="permalink">MAS Emergence Safety
|
||||
</a>
|
||||
|
||||
</h2>
|
||||
|
||||
|
||||
<p class="page__meta">
|
||||
|
||||
|
||||
|
||||
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||||
|
||||
|
||||
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||||
|
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<span class="page__meta-readtime">
|
||||
<i class="far fa-clock" aria-hidden="true"></i>
|
||||
|
||||
1 minute read
|
||||
|
||||
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|
||||
|
||||
</p>
|
||||
|
||||
|
||||
<p class="archive__item-excerpt" itemprop="description" style="text-align: left;">
|
||||
Formalized MAS emergence misalignment; proposed safety mitigation strategies.
|
||||
|
||||
</p>
|
||||
</div>
|
||||
</article>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="list__item">
|
||||
<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork" style="display: flex; align-items: flex-start;">
|
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||||
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||||
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||||
|
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|
||||
<div class="archive__item-teaser-list" style="margin-right: 1em; flex-shrink: 0;">
|
||||
<img src="/assets/figures/20_aquarium.png" alt="" style="display: block;">
|
||||
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|
||||
|
||||
|
||||
|
||||
<div style="flex-grow: 1;">
|
||||
<h2 class="archive__item-title no_toc" itemprop="headline" style="text-align: left !important;">
|
||||
|
||||
<a href="/research/aquarium-marl-environment/" rel="permalink">Aquarium MARL Environment
|
||||
</a>
|
||||
|
||||
</h2>
|
||||
|
||||
|
||||
<p class="page__meta">
|
||||
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|
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<span class="page__meta-readtime">
|
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<i class="far fa-clock" aria-hidden="true"></i>
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1 minute read
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||||
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||||
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||||
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||||
</p>
|
||||
|
||||
|
||||
<p class="archive__item-excerpt" itemprop="description" style="text-align: left;">
|
||||
Aquarium: Open-source MARL environment for predator-prey studies.
|
||||
|
||||
</p>
|
||||
</div>
|
||||
</article>
|
||||
</div>
|
||||
|
||||
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||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
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<div class="list__item">
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<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork" style="display: flex; align-items: flex-start;">
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||||
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||||
|
||||
|
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||||
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||||
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|
||||
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|
||||
|
||||
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||||
|
||||
<div style="flex-grow: 1;">
|
||||
<h2 class="archive__item-title no_toc" itemprop="headline" style="text-align: left !important;">
|
||||
|
||||
<a href="/projects/server-administration/" rel="permalink">LMU DevOps Admin
|
||||
</a>
|
||||
|
||||
</h2>
|
||||
|
||||
|
||||
<p class="page__meta">
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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||||
|
||||
<span class="page__meta-readtime">
|
||||
<i class="far fa-clock" aria-hidden="true"></i>
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|
||||
2 minute read
|
||||
|
||||
</span>
|
||||
|
||||
</p>
|
||||
|
||||
|
||||
<p class="archive__item-excerpt" itemprop="description" style="text-align: left;">
|
||||
Managed LMU chair IT: Kubernetes, CI/CD, automation (2018-2023).
|
||||
|
||||
</p>
|
||||
</div>
|
||||
</article>
|
||||
</div>
|
||||
|
||||
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||||
|
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|
||||
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||||
|
||||
|
||||
|
||||
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<article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork" style="display: flex; align-items: flex-start;">
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||||
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="archive__item-teaser-list" style="margin-right: 1em; flex-shrink: 0;">
|
||||
<img src="/assets/figures/19_binary_primates_teaser.jpg" alt="" style="display: block;">
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div style="flex-grow: 1;">
|
||||
<h2 class="archive__item-title no_toc" itemprop="headline" style="text-align: left !important;">
|
||||
|
||||
<a href="/research/primate-subsegment-sorting/" rel="permalink">Primate Subsegment Sorting
|
||||
</a>
|
||||
|
||||
</h2>
|
||||
|
||||
|
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<p class="page__meta">
|
||||
|
||||
|
||||
|
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|
||||
<span class="page__meta-readtime">
|
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<i class="far fa-clock" aria-hidden="true"></i>
|
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|
||||
1 minute read
|
||||
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</span>
|
||||
|
||||
</p>
|
||||
|
||||
|
||||
<p class="archive__item-excerpt" itemprop="description" style="text-align: left;">
|
||||
Binary subsegment presorting improves noisy primate sound classification.
|
||||
|
||||
</p>
|
||||
</div>
|
||||
</article>
|
||||
</div>
|
||||
|
||||
|
||||
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|
||||
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|
||||
|
||||
|
||||
|
||||
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Reference in New Issue
Block a user