rebase from mnml mistakes source

.gitattributes

@@ -4,7 +4,7 @@ assets/js/lunr/*                linguist-vendored
 assets/js/plugins/*             linguist-vendored
 assets/js/vendor/*              linguist-vendored
 _sass/minimal-mistakes/vendor/* linguist-vendored
-CHANGELOG.md                    text merge=ours
+CHANGELOG.md                    merge=ours
-docs/_docs/18-history.md        text merge=ours
+docs                            merge=ours
-
+_posts                          merge=ours
 *.md                            text

.gitignore

@@ -30,5 +30,15 @@ codekit-config.json
 .sass-cache
 _asset_bundler_cache
 _site
+<<<<<<< HEAD
 docs
 CHANGELOG.MD
+=======
+docs/Rakefile
+docs/_data/theme.yml
+docs/_docs/22-faq.md
+docs/_includes/after-content.html
+docs/_includes/before-related.html
+docs/_includes/comments-providers/scripts.html
+docs/_posts/2009-10-06-edge-case-broken-highlighting.md
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

Gemfile

@@ -7,7 +7,10 @@ source "https://rubygems.org"
 #
 # This will help ensure the proper Jekyll version is running.
 # Happy Jekylling!
+<<<<<<< HEAD
 gem "json"
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 gem "jekyll"
 gem "minimal-mistakes-jekyll"
 
@@ -32,5 +35,8 @@ group :jekyll_plugins do
   gem 'jemoji'
   gem 'jekyll-archives'
   gem 'jekyll-include-cache'
+<<<<<<< HEAD
   gem 'jekyll-webp'
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 end

_bibliography.bib

@@ -100,11 +100,18 @@ organization={Conference on Artificial Life - Alife 2023},
   publisher={Copernicus Publications Göttingen, Germany}
 }
 
+<<<<<<< HEAD
 @inproceedings{illium2020surgical,
   title={Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms},
   author={Illium, Steffen and M{\"u}ller, Robert and Sedlmeier, Andreas and Linnhoff-Popien, Claudia},
   booktitle={Proc. Interspeech 2020},
   pages={2052--2056},
+=======
+@article{illium2020surgical,
+  title={Surgical mask detection with convolutional neural networks and data augmentations on spectrograms},
+  author={Illium, Steffen and Müller, Robert and Sedlmeier, Andreas and Linnhoff-Popien, Claudia},
+  journal={arXiv preprint arXiv:2008.04590},
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
   year={2020}
 }
 
@@ -194,6 +201,7 @@ organization={Conference on Artificial Life - Alife 2023},
   organization={Springer International Publishing Cham}
 }
 
+<<<<<<< HEAD
 @inproceedings{illium2022constructing,
   title={Constructing organism networks from collaborative self-replicators},
   author={Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and K{\"o}lle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},
@@ -211,6 +219,20 @@ organization={Conference on Artificial Life - Alife 2023},
   number={Volume 3},
   pages={350--357},
   year={2023}
+=======
+@article{illium2022constructing,
+  title={Constructing Organism Networks from Collaborative Self-Replicators},
+  author={Illium, Steffen and Zorn, Maximilian and Kölle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},
+  journal={arXiv preprint arXiv:2212.10078},
+  year={2022}
+}
+
+@article{illium2022voronoipatches,
+  title={VoronoiPatches: Evaluating A New Data Augmentation Method},
+  author={Illium, Steffen and Griffin, Gretchen and Kölle, Michael and Zorn, Maximilian and Nü{\ss}lein, Jonas and Linnhoff-Popien, Claudia},
+  journal={arXiv preprint arXiv:2212.10054},
+  year={2022}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 }
 
 @article{kolle2023compression,
@@ -218,6 +240,7 @@ organization={Conference on Artificial Life - Alife 2023},
   author={Kölle, Michael and Illium, Steffen and Hahn, Carsten and Schauer, Lorenz and Hutter, Johannes and Linnhoff-Popien, Claudia},
   journal={arXiv preprint arXiv:2301.07420},
   year={2023}
+<<<<<<< HEAD
 }
 
 @inproceedings{altmann2024emergence,
@@ -234,4 +257,6 @@ organization={Conference on Artificial Life - Alife 2023},
   author={Kölle, Michael and Erpelding, Yannick and Ritz, Fabian and Phan, Thomy and Illium, Steffen and Linnhoff-Popien, Claudia},
   journal={arXiv preprint arXiv:2401.07056},
   year={2024}
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 }

_config.yml

@@ -14,7 +14,7 @@
 theme                    : "minimal-mistakes-jekyll"
 
 # remote_theme           : "mmistakes/minimal-mistakes"
-minimal_mistakes_skin    : "default" # "air", "aqua", "contrast", "dark", "dirt", "neon", "mint", "plum", "sunrise"
+minimal_mistakes_skin    : "dark" # "air", "aqua", "contrast", "dark", "dirt", "neon", "mint", "plum", "sunrise"
 
 # Site Settings
 locale                   : "en-US"
@@ -26,10 +26,17 @@ description              : "Personal Website"
 url                      : "https://steffenillium.de" # the base hostname & protocol for your site e.g. "https://mmistakes.github.io"
 baseurl                  : "" # the subpath of your site, e.g. "/blog"
 repository               : "illiumst" # GitHub username/repo-name e.g. "mmistakes/minimal-mistakes"
+<<<<<<< HEAD
 teaser                   : "/assets/images/newshot_2.jpg" # path of fallback teaser image, e.g. "/assets/images/500x300.png"
 logo                     : # path of logo image to display in the masthead, e.g. "/assets/images/88x88.png"
 masthead_title           : "portfolio" # overrides the website title displayed in the masthead, use " " for no title
 breadcrumbs              : false # true, false (default)
+=======
+teaser                   : "/assets/images/headshot.jpg" # path of fallback teaser image, e.g. "/assets/images/500x300.png"
+logo                     : # path of logo image to display in the masthead, e.g. "/assets/images/88x88.png"
+masthead_title           : "portfolio" # overrides the website title displayed in the masthead, use " " for no title
+breadcrumbs              : true # true, false (default)
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 words_per_minute         : 200
 reCaptcha:
   siteKey                :
@@ -50,18 +57,28 @@ analytics:
   provider: "custom"
 
 # Social Sharing
+<<<<<<< HEAD
 og_image                 : "/assets/images/newshot_2.jpg" # Open Graph/Twitter default site image
 # For specifying social profiles
 # - https://developers.google.com/structured-data/customize/social-profiles
 social:
   type                   : Person
   name                   : Steffen Illium
+=======
+og_image                 : "/assets/images/headshot.jpg" # Open Graph/Twitter default site image
+# For specifying social profiles
+# - https://developers.google.com/structured-data/customize/social-profiles
+social:
+  type                   : # Person or Organization (defaults to Person)
+  name                   : # If the user or organization name differs from the site's name
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
   links                  : # An array of links to social media profiles
     - https://www.linkedin.com/in/steffen-illium/
 
 # Site Author
 author:
   name             : "Steffen Illium"
+<<<<<<< HEAD
   avatar           : "/assets/images/newshot_2.jpg" # path of avatar image, e.g. "/assets/images/bio-photo.jpg"
   bio              : "[PhD. in Comp. Science](https://www.mobile.ifi.lmu.de/team/steffen-illium/) <br>[AI Consultant & Researcher](/research/)"
   location         : "Augsburg"
@@ -71,15 +88,33 @@ author:
       url: "https://www.mobile.ifi.lmu.de/team/steffen-illium/"
     - label: "Scholar"
       icon: "fab fa-google-scholar"
+=======
+  avatar           : "/assets/images/headshot.jpg" # path of avatar image, e.g. "/assets/images/bio-photo.jpg"
+  bio              : "[AI Research](/research/) and [Lecturer](/teaching/) as [PHD Student](https://www.mobile.ifi.lmu.de/team/steffen-illium/) @ [LMU Munich](https://www.lmu.de/en/index.html)"
+  location         : "Augsburg"
+  links:
+    - label: "LMU-Munich"
+      icon: "fa fa-link"
+      url: "https://www.mobile.ifi.lmu.de/team/steffen-illium/"
+    - label: "Scholar"
+      icon: "ai ai-google-scholar"
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
       url: "https://scholar.google.de/citations?hl=en&pli=1&user=NODAd94AAAAJ"
     - label: "Arxive"
       icon: "ai ai-arxiv"
       url: "https://arxiv.org/a/illium_s_1.html"
     - label: "Researchgate"
+<<<<<<< HEAD
       icon: "fab fa-researchgate"
       url: "https://www.researchgate.net/profile/Steffen-Illium"
 
 
+=======
+      icon: "ai ai-researchgate"
+      url: "https://www.researchgate.net/profile/Steffen-Illium"
+
+    
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 
 # Site Footer
 footer:
@@ -90,9 +125,15 @@ footer:
     - label: "LinkedIn"
       icon: "fab fa-fw fa-linkedin"
       url: "https://www.linkedin.com/in/steffen-illium/"
+<<<<<<< HEAD
     - label: "Gitea"
       icon: "fab fa-git-alt"
       url: "https://gitea.steffenillium.de/steffen"
+=======
+    - label: "GitHub"
+      icon: "fab fa-fw fa-github"
+      url: "https://github.com/illiumst"
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 
 
 # Reading Files
@@ -204,7 +245,10 @@ plugins:
   - jekyll-scholar
   - jekyll-data
   - jekyll-archives
+<<<<<<< HEAD
   - jekyll-webp
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
   - jemoji
 
 # mimic GitHub Pages with --safe
@@ -213,7 +257,7 @@ whitelist:
   - jekyll-sitemap
   - jekyll-gist
   - jekyll-feed
-  - jekyll-include-cache
+   jekyll-include-cache
 
 
 # Archives
@@ -265,6 +309,7 @@ defaults:
       read_time: false
       comments: false
       share: false
+<<<<<<< HEAD
       related: true
       show_date: false
 
@@ -280,12 +325,21 @@ scholar:
   bibliography: _bibliography.bib
   repository: "./assets/publications"
   bibliography_template: bibtemplate
+=======
+      related: false
+      show_date: true
+
+scholar:
+  style: modern-language-association
+  bibliography: _bibliography.bib
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
   source: ""
   sort_by: year,month
   order: descending
   group_by: year
   group_order: descending
   relative: "/publications"
+<<<<<<< HEAD
 
 ############################################################
 # Site configuration for the WebP Generator Plugin
@@ -324,4 +378,6 @@ webp:
   # append '.webp' to filename after original extension rather than replacing it.
   # Default transforms `image.png` to `image.webp`, while changing to true transforms `image.png` to `image.png.webp`
   append_ext: true
-############################################################
+############################################################
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_data/navigation.yml

@@ -1,4 +1,5 @@
 main:
+<<<<<<< HEAD
   - title: "publications"
     url: /publications
   - title: "research"
@@ -10,4 +11,18 @@ main:
   # - title: "Blog"
   #   url: /blog
   - title: "about me"
-    url: /about
+    url: /about
+=======
+  - title: "teaching"
+    url: /teaching
+  #- title: "Projects"
+  #  url: /projects
+  - title: "research"
+    url: /research
+  # - title: "Blog"
+  #   url: /blog
+  - title: "publications"
+    url: /publications
+  #- title: "CV"
+  #  url: /cv
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2018-02-01-innomi.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "InnoMi Project"
 categories: projects
 excerpt: "Early-stage mobile/distributed tech transfer between academia and industry (Bavaria)."
@@ -29,3 +30,16 @@ header:
 *   **Dissemination & Editorial Leadership:** To further bridge the gap between cutting-edge digitalization trends and industry practitioners, I served as the Head of the Online Editorial Team for the associated [Digitale Welt Magazin (DW)](https://digitaleweltmagazin.de/) from 2018 to 2023, a role supported by the InnoMi initiative.
 
 This project provided a platform not only for advancing research but also for developing crucial skills in project communication, event management, and editorial leadership, directly contributing to the technology transfer goals of the Bavarian region.
+=======
+title:  "Mobile Internet Innovations"
+categories: projects
+excerpt: "Aiming to make Bavaria more economically strong by transferring innovations from the university to industry at an early stage."
+header:
+  teaser: assets/images/projects/innomi.png
+
+---
+
+![logo](/assets/images/projects/innomi.png){: .align-left style="padding:0.1em; width:5em"}
+
+
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2019-02-24-dw.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "DW Editorial Lead"
 categories: projects
 excerpt: "Led online editorial team for DIGITALE WELT Magazin (2018-2023)."
@@ -39,4 +40,15 @@ Prior to leading the online team, I contributed to the print editions of the mag
   <img src="/assets/images/projects/dw_magazin.png" alt="Cover collage of printed DIGITALE WELT Magazin issues" width="550">
   <figcaption>Examples of DIGITALE WELT Print Magazine Covers</figcaption>
 </center>
-<br>
+<br>
+=======
+title:  "Leading an editorial office."
+categories: projects
+excerpt: "A unique line of text to describe this post that will display in an archive listing and meta description with SEO benefits."
+header:
+  teaser: assets/images/projects/dw.png
+
+---
+
+![logo](\assets\images\projects\dw.png){: .align-left style="padding:0.1em; width:5em"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2019-02-24-water-networks.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "ErLoWa Leak Detection"
 categories: projects
 excerpt: "Deep learning detects acoustic water leaks with SWM."
@@ -59,3 +60,15 @@ In collaboration with Munich's municipal utility provider, Stadtwerke München (
 This applied research project provided valuable experience in handling real-world sensor data, adapting machine learning models for specific industrial challenges, and collaborating effectively with industry partners.
 
 {% include reference.html %}
+=======
+title:  "Detection and localization of leakages in water networks."
+categories: projects
+excerpt: "A u"
+tags: acoustic anomaly-detection 
+header:
+  teaser: assets/images/projects/pipe_leak.png
+
+---
+
+![Leaking pipe image](/assets/images/projects/pipe_leak.png){: .align-left style="padding:0.1em; width:5em"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2019-10-15-OpenMunich.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "OpenMunich Conference Organization"
 categories: projects
 tags: community-engagement
@@ -33,4 +34,15 @@ The OpenMunich conference series during 2016 until 2019 offered a platform to sh
 *   **Website & Communication:** Managed the official conference website openmunich.eu (offline), including content creation, structural design, updates, and maintenance. Handled external communications and promotions.
 *   **Sponsorship Liaison:** Coordinated with Accenture and Red Hat regarding their sponsorship contributions and participation requirements.
 
-This role required organizational skills, effective communication across diverse stakeholder groups, and project management to ensure the successful delivery of the conference.
+This role required organizational skills, effective communication across diverse stakeholder groups, and project management to ensure the successful delivery of the conference.
+=======
+title:  "OpenMunich.eu - Conference Organisation"
+categories: acoustic anomaly-detection projects
+excerpt: "Organization a Munich based open-souce conference with Red Hat and Accenture"
+header:
+  teaser: assets/images/projects/openmunich.png
+
+---
+
+![logo](\assets\images\projects\openmunich.png){: .align-left style="padding:0.1em; width:5em"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2020-05-01-FIKS.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "AI-Fusion Safety"
 categories: projects
 tags: multi-agent-systems reinforcement-learning safety emergence simulation
@@ -67,3 +68,14 @@ To facilitate research into these phenomena, key contributions included the deve
 This project involved close collaboration with industry-focused researchers, software development adhering to modern standards, and deep investigation into the theoretical underpinnings of emergence and safety in MARL systems. The developed tools provide a valuable platform for continued research in this critical area.
 
 {% include reference.html %}
+=======
+title:  "AI-Fusion: Emergence Detection for mixed MARL systems."
+categories: acoustic anomaly-detection projects
+excerpt: "Bringing together agents can be an inherent safety problem. Building the basis to mix and match."
+header:
+  teaser: assets/images/projects/robot.png
+
+---
+
+![logo](\assets\images\projects\robot.png){: .align-left style="padding:0.1em; width:5em"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/projects/2023-10-15-server-administration.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "LMU DevOps Admin"
 categories: projects
 tags: devops kubernetes server-administration infrastructure
@@ -50,4 +51,13 @@ During my tenure at the LMU Chair for Mobile and Distributed Systems, alongside
 
 This hands-on role provided deep practical experience in modern system administration, networking, Infrastructure as Code (IaC), and cloud-native technologies within an academic research setting. It fostered my preference for minimalist, reproducible, and microservice-oriented architectures. These principles and skills are actively applied in my personal projects, including the self-hosting and management of this website and various other containerized services.
 
-A more comprehensive list of the technologies I work with can be found on the [About Me](/about/) page.
+A more comprehensive list of the technologies I work with can be found on the [About Me](/about/) page.
+=======
+title:  "Linux Server Administration"
+categories: projects server_admin unix
+excerpt: "Linux Server Administration (Workstations and Web)"
+header:
+  teaser: assets/images/projects/arch.png
+---
+![logo](\assets\images\projects\arch.png){: .align-left style="padding:0.1em; width:5em"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/research/2018-11-01-trajectory-annotation.md

@@ -0,0 +1,13 @@
+---
+layout: single
+title:  "Trajectory annotation by spatial perception"
+categories: research 
+excerpt: "We propose an approach to annotate trajectories using sequences of spatial perception."
+header:
+  teaser: assets/figures/0_trajectory_reconstruction_teaser.png
+---
+
+![Isovist Concept](\assets\figures\0_trajectory_isovist.jpg){:style="display:block; margin-left:auto; margin-right:auto; width:350px"}
+In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the verbal communication and the interaction between robotic units and/or humans, reliable and robust systems w.r.t. noise and processing results are needed. This work builds a foundation to address this task. By using a continuous representation of spatial perception in interiors learned from trajectory data, our approach clusters movement in dependency to its spatial context. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of spatio-temporal trajectory data. This learned encoding can be used to form prototypical representations. We present promising results that clear the path for future applications. {% cite feld2018trajectory %}
+
+![Trajectory Reconstruction](\assets\figures\0_trajectory_reconstruction.jpg){:style="display:block; margin-left:auto; margin-right:auto; width:350px"}

_posts/research/2019-07-01-self-replication.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Self-Replication in Neural Networks"
+categories: research 
+excerpt: "Introduction of NNs that are able to replicate their own weights."
+header:
+  teaser: assets/figures/1_self_replication_pca_space.jpg
+---
+
+![Self-Replication Robustness](\assets\figures\1_self_replication_robustness.jpg){:style="display:block; margin-left:auto; margin-right:auto; width:350px"}
+
+The foundation of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. We argue that backpropagation is the natural way to navigate the space of network weights and show how it allows non-trivial self-replicators to arise naturally. We then extend the setting to construct an artificial chemistry environment of several neural networks.
+{% cite gabor2019self %}
+
+![Self-replicators in PCA Space (Soup)](\assets\figures\1_self_replication_pca_space.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2019-07-05-deep-neural-baselines.md

@@ -0,0 +1,12 @@
+---
+layout: single
+title:  "Deep-Neural Baseline"
+categories: research 
+excerpt: "Introduction a deep baseline for audio classification."
+header:
+  teaser: assets/figures/3_deep_neural_baselines_teaser.jpg
+---
+
+![Self-Replication Robustness](\assets\figures\3_deep_neural_baselines.jpg){:style="display:block; margin-left:auto; margin-right:auto; width:250px"}
+Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-end deep learning approach to detect and classify patterns reflecting sleepiness in the human voice. Our approach is based solely on a moderately complex deep neural network architecture. It may be applied directly on the audio data without requiring any specific feature engineering, thus remaining transferable to other audio classification tasks. Nevertheless, our approach performs similar to state-of-the-art machine learning models.
+{% cite elsner2019deep %}

_posts/research/2020-03-01-soccer-team-vectors.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "Soccer Team Vectors"
 categories: research
 tags: machine-learning representation-learning sports-analytics similarity-search
@@ -20,4 +21,17 @@ The utility of these learned representations is demonstrated through several dow
 *   **Similarity Search:** The vector space allows for efficient identification of teams similar to a given query team based on proximity.
 *   **Team Ranking:** The embeddings provide a basis for generating data-driven team rankings.
 
-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 {% cite muller2020soccer %}.
+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 {% cite muller2020soccer %}.
+=======
+title:  "Learning Soccer-Team Vecors"
+categories: research 
+excerpt: "Team market value estimation, similarity search and rankings."
+header:
+  teaser: assets/figures/2_steve_algo.jpg
+---
+
+In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.
+{% cite muller2020soccer %}
+
+![STEVE Algorithm](\assets\figures\2_steve_algo.jpg){:style="display:block; margin-left:auto; margin-right:auto;"}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/research/2020-05-01-hybrid-poin-cloud-segmentation.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Point Cloud Segmentation"
+categories: research 
+excerpt: "Segmetation of point clouds into primitive building blocks."
+header:
+  teaser: assets/figures/4_point_cloud_segmentation_teaser.jpg
+---
+
+![Point Cloud Segmentation](\assets\figures\4_point_cloud_pipeline.jpg){:style="display:block; margin-left:auto; margin-right:auto;"}
+
+The segmentation and fitting of solid primitives to 3D point clouds is a complex task. Existing systems are restricted either in the number of input points or the supported primitive types. This paper proposes a hybrid pipeline that is able to reconstruct spheres, bounded cylinders and rectangular cuboids on large point sets. It uses a combination of deep learning and classical RANSAC for primitive fitting, a DBSCAN-based clustering scheme for increased stability and a specialized Genetic Algorithm for robust cuboid extraction. In a detailed evaluation, its performance metrics are discussed and resulting solid primitive sets are visualized. The paper concludes with a discussion of the approach’s limitations.
+{% cite friedrich2020hybrid %}
+
+![Point Cloud Segmentation](\assets\figures\4_point_cloud_segmentation.jpg){:style="display:block; margin-left:auto; margin-right:auto;"}

_posts/research/2020-06-01-ood-classification.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Policy Entropy for OOD Classification"
+categories: research 
+excerpt: "PEOC for reliably detecting unencountered states in deep RL"
+header:
+  teaser: assets/figures/6_ood_pipeline.jpg
+---
+
+![PEOC Pipeline](\assets\figures\6_ood_pipeline.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.
+{% cite sedlmeier2020peoc %}
+
+![PEOC Performance](\assets\figures\6_ood_performance.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2020-07-15-what-to-do-in-the-meantime.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "What to do in the Meantime"
+categories: research 
+excerpt: "Service Coverage Analysis for Parked Autonomous Vehicles"
+header:
+  teaser: assets/figures/5_meantime_coverage.jpg
+---
+
+![Estimated Service Coverage](assets\figures\5_meantime_coverage.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+Fully autonomously driving vehicles are expected to be a widely available technology in the near future. Privately owned cars, which remain parked for the majority of their lifetime, may therefore be capable of driving independently during their usual long parking periods (e.g. their owners working hours). Our analysis aims to focus on the potential of a privately owned shared car concept as transition period between the present usages of privately owned cars towards a transportation paradigm of privately owned shared autonomous vehicles. We propose two methods in the field of reachability analysis to evaluate the impact of such vehicles during parking periods. Our proposed methods are applied to a dataset of parking times of users of a telematics service provider in the Munich area (Germany). We show the impact of time and location dependent effects on the analyzed service coverage, such as business week rush hours or cover age divergence between urban and suburban regions.
+{% cite illium2020meantime %}
+
+![Parked Vehicle Availability](\assets\figures\5_meantime_availability.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2020-10-25-surgical-mask-detection copy.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Surgical Mask Detection"
+categories: research audio deep-learning
+excerpt: "Convolutional Neural Networks and Data Augmentations on Spectrograms"
+header:
+  teaser: assets/figures/7_mask_models.jpg
+---
+
+![PEOC Pipeline](\assets\figures\7_mask_mels.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+In many fields of research, labeled data-sets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network engineering and classification tasks. The idea here is to reduce model over-fitting to the feature distribution of a small under-descriptive training data-set. We try to evaluate such data augmentation techniques to gather insights in the performance boost they provide for several convolutional neural networks on mel-spectrogram representations of audio data. We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice. Also we consider four varying architectures to account for augmentation robustness. Results show that most of the baselines given by ComParE are outperformed
+{% cite illium2020surgical %}
+
+![Models](\assets\figures\7_mask_models.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2021-03-01-Feature_Representations_Anomalous_Sound.md

@@ -0,0 +1,13 @@
+---
+layout: single
+title:  "Anomalous Sound Detection"
+categories: research audio deep-learning anomalie-detection
+excerpt: "Analysis of Feature Representations for Anomalous Sound Detection"
+header:
+  teaser: assets/figures/8_anomalous_sound_teaser.jpg
+---
+
+![Pipeline](\assets\figures\8_anomalous_sound_features.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+The problem of Constructive Solid Geometry (CSG) tree reconstruction from 3D point clouds or 3D triangle meshes is hard to solve. At first, the input data set (point cloud, triangle soup or triangle mesh) has to be segmented and geometric primitives (spheres, cylinders, ...) have to be fitted to each subset. Then, the size- and shape optimal CSG tree has to be extracted. We propose a pipeline for CSG reconstruction consisting of multiple stages: A primitive extraction step, which uses deep learning for primitive detection, a clustered variant of RANSAC for parameter fitting, and a Genetic Algorithm (GA) for convex polytope generation. It directly transforms 3D point clouds or triangle meshes into solid primitives. The filtered primitive set is then used as input for a GA-based CSG extraction stage. We evaluate two different CSG extraction methodologies and furthermore compare our pipeline to current state-of-the-art methods.
+{% cite muller2020analysis %}

_posts/research/2021-03-02-AD_by_Image_Transfer_Learning.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Anomalous Image Transfer"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning"
+header:
+  teaser: assets/figures/9_image_transfer_sound_teaser.jpg
+---
+
+![Mels](\assets\figures\9_image_transfer_sound_mels.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
+{% cite muller2020acoustic %}
+
+![Workflow](\assets\figures\9_image_transfer_sound_workflow.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2021-03-03-Water_Networks.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Acoustic Leak Detection"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Anomalie based Leak Detection in Water Networks"
+header:
+  teaser: assets/figures/10_water_networks_teaser.jpg
+---
+
+![Leak-Mels](\assets\figures\10_water_networks_mel.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
+{% cite muller2021acoustic %}
+
+![Approach](\assets\figures\10_water_networks_approach.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2021-03-04-Recurrent_Primate_Classification.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Primate Vocalization Classification"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "A Deep and Recurrent Architecture"
+header:
+  teaser: assets/figures/11_recurrent_primate_workflow.jpg
+---
+
+![Leak-Mels](\assets\figures\11_recurrent_primate_workflow.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+Wildlife monitoring is an essential part of most conservation efforts where one of the many building blocks is acoustic monitoring. Acoustic monitoring has the advantage of being noninvasive and applicable in areas of high vegetation. In this work, we present a deep and recurrent architecture for the classification of primate vocalizations that is based upon well proven modules such as bidirectional Long Short-Term Memory neural networks, pooling, normalized softmax and focal loss. Additionally, we apply Bayesian optimization to obtain a suitable set of hyperparameters. We test our approach on a recently published dataset of primate vocalizations that were recorded in an African wildlife sanctuary.
+{% cite muller2021deep %}
+
+![Approach](\assets\figures\11_recurrent_primate_results.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2021-03-05-Vision_Transformer.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Mel-Vision Transformer"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Attention based audio classification on Mel-Spektrograms"
+header:
+  teaser: assets/figures/12_vision_transformer_teaser.jpg
+---
+
+![Leak-Mels](\assets\figures\12_vision_transformer_data.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve comparable performance on both (PRS and CCS challenge) tasks of ComParE21, outperforming most single model baselines. We further introduce overlapping vertical patching and evaluate the influence of parameter configurations.
+{% cite illium2021visual %}
+
+![Approach](\assets\figures\12_vision_transformer_models.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2021-03-06-SR_Goals.md

@@ -0,0 +1,13 @@
+---
+layout: single
+title:  "Self-Replication Goals"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Combining replication and auxiliary task for neural networks."
+header:
+  teaser: assets/figures/13_sr_teaser.jpg
+---
+
+![Self-Replicator Analysis](\assets\figures\13_sr_analysis.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+Self-replicating neural networks can be trained to output a representation of themselves, making them navigate towards non-trivial fixpoints in their weight space. We explore the problem of adding a secondary functionality to the primary task of replication. We find a successful solution in training the networks with separate input/output vectors for one network trained in both tasks so that the additional task does not hinder (and even stabilizes) the self-replication task. Furthermore, we observe the interaction of our goal-networks in an artificial chemistry environment. We examine the influence of different action parameters on the population and their effects on the group’s learning capability. Lastly we show the possibility of safely guiding the whole group to goal-fulfilling weight configurations via the inclusion of one specially-developed guiding particle that is able to propagate a secondary task to its peers.
+{% cite gabor2021goals %}

_posts/research/2022-05-09-AD_in_RL.md

@@ -0,0 +1,13 @@
+---
+layout: single
+title:  "Anomaly Detection in RL"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Towards Anomaly Detection in Reinforcement Learning"
+header:
+  teaser: assets/figures/14_ad_rl_teaser.jpg
+---
+
+![Formal Definition](\assets\figures\14_ad_rl.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+Identifying datapoints that substantially differ from normality is the task of anomaly detection (AD). While AD has gained widespread attention in rich data domains such as images, videos, audio and text, it has has been studied less frequently in the context of reinforcement learning (RL). This is due to the additional layer of complexity that RL introduces through sequential decision making. Developing suitable anomaly detectors for RL is of particular importance in safety-critical scenarios where acting on anomalous data could result in hazardous situations. In this work, we address the question of what AD means in the context of RL. We found that current research trains and evaluates on overly simplistic and unrealistic scenarios which reduce to classic pattern recognition tasks. We link AD in RL to various fields in RL such as lifelong RL and generalization. We discuss their similarities, differences, and how the fields can benefit from each other. Moreover, we identify non-stationarity to be one of the key drivers for future research on AD in RL and make a first step towards a more formal treatment of the problem by framing it in terms of the recently introduced block contextual Markov decision process. Finally, we define a list of practical desiderata for future problems.
+{% cite muller2022towards %}

_posts/research/2022-08-01-SR_journal.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Self-Replication in NNs"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Elaboration and journal article of the initial paper"
+header:
+  teaser: assets/figures/15_sr_journal_teaser.jpg
+---
+
+![Children Evolution](\assets\figures\15_sr_journal_children.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
+{% cite gabor2022self %}
+
+![Noise Levels](\assets\figures\15_noise_levels.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2022-12-01-organism_networks.md

@@ -0,0 +1,15 @@
+---
+layout: single
+title:  "Organism Networks"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Constructing ON from Collaborative Self-Replicators"
+header:
+  teaser: assets/figures/16_on_teaser.jpg
+---
+
+![Organism Network Architecture](\assets\figures\16_on_architecture.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
+{% cite illium2022constructing %}
+
+![Dropout](\assets\figures\16_on_dropout.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2023-02-24-voronoi_patches.md

@@ -0,0 +1,17 @@
+---
+layout: single
+title:  "Voronoi Patches"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Evaluating A New Data Augmentation Method"
+header:
+  teaser: assets/figures/17_vp_teaser.jpg
+---
+
+![Organism Network Architecture](\assets\figures\17_vp_lion.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augmentation methods (DA) have been proposed to supplement or generate more training data, and thereby increase its quality. In this work, we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches. Unlike other DA methods, VP uses small convex polygon-shaped patches in a random layout to transport information around within an image. Sudden transitions created between patches and the original image can, optionally, be smoothed. In our experiments, VP outperformed current DA methods regarding model variance and overfitting tendencies. We demonstrate data augmentation utilizing non-linear re-combination of information within images, and non-orthogonal shapes and structures improves CNN model robustness on unseen data.
+{% cite illium2022voronoipatches %}
+
+:trophy: This paper won the conference's [Best Poster Award](https://icaart.scitevents.org/PreviousAwards.aspx?y=2024#2023), which is a special honor. :trophy:
+
+![Dropout](\assets\figures\17_vp_results.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/research/2023-05-01-surprised_soup.md

@@ -0,0 +1,16 @@
+---
+layout: single
+title:  "Social NN-Soup"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Social interaction based on surprise minimization"
+header:
+  teaser: assets/figures/18_surprised_soup_teaser.jpg
+---
+
+![Social Soup Schematics](\assets\figures\18_surprised_soup_schematic.jpg){: .align-right style="padding:2em; width:20em"}
+
+A recent branch of research in artificial life has constructed artificial chemistry systems whose particles are dynamic neural networks. These particles can be applied to each other and show a tendency towards self-replication of their weight values. We define new interactions for said particles that allow them to recognize one another and learn predictors for each other’s behavior. For instance, each particle minimizes its surprise when observing another particle’s behavior. Given a special catalyst particle to exert evolutionary selection pressure on the soup of particles, these ‘social’ interactions are sufficient to produce emergent behavior similar to the stability pattern previously only achieved via explicit self-replication training.
+{% cite zorn23surprise %}
+
+![Soup Trajectories](\assets\figures\18_surprised_soup_trajec.jpg){:style="display:block; margin-left:auto; margin-right:auto;"}
+

_posts/research/2023-06-25-binary_primates.md

@@ -0,0 +1,17 @@
+---
+layout: single
+title:  "Binary Presorting"
+categories: research audio deep-learning anomalie-detection 
+excerpt: "Improving Primate Sounds Classification"
+header:
+  teaser: assets/figures/19_binary_primates_teaser.jpg
+---
+
+![Multiclass Training Pipeline](\assets\figures\19_binary_primates_pipeline.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging ComparE 2021 dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines.
+{% cite koelle23primate %}
+
+![Thresholding](\assets\figures\19_binary_primates_thresholding.jpg){:style="display:block; margin-left:auto; margin-right:auto"}
+
+![Thresholding](\assets\figures\19_binary_primates_results.jpg){:style="display:block; margin-left:auto; margin-right:auto"}

_posts/teaching/2018-02-15-computer_achitecture.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "Computer Architecture TA"
 categories: teaching
 excerpt: "TA & Coordinator, LMU Computer Architecture course."
@@ -49,3 +50,37 @@ The course provided students with a comprehensive introduction to the fundamenta
   </div>
   <div style="clear: both;"></div>
 </div>
+=======
+title:  "Lecture: Computer Architectures"
+categories: teaching
+excerpt: "Assisting to manage a lecture about the technical foundations of computer science."
+header:
+  teaser: assets/images/teaching/computer_gear.png
+---
+
+![logo](\assets\images\teaching\computer_gear.png){: .align-left style="padding:0.1em; width:5em"}In the semesters listed below, my job was to assist in organiszing this bachelors lecture of about 600 students.
+We had a team of 10-12 tutors that were employed to balance the workload.
+Also, we created each weeks graded exercise sheets as well as the written exam and organized it.
+
+### Contents
+<div class="table-right">
+
+| [Summer semester 2019](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/rechnerarchitektur-sose19/)| [Summer semester 2018](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/rechnerarchitektur-sose18/)|
+
+</div>This lecture provided an introduction to the technical foundations of computer science and the architecture of computers.
+Topics introduced in the lecture include representation of information in computers, classical components of a computer, arithmetic in computers, logical design of computers, switching circuits, representation of memory contents, primary and secondary memories, input and output, and pipelining.
+More concrete:
+
+- Representation as bits: (numbers, text, images, audio, video, programs).
+- Storage and Transfer of data, error detection and correction
+- Boolean algebra
+- Processing of data: circuit design, switching networks
+- Number representation and arithmetic
+- Switching functions, switching networks, switching plants
+- Von Neumann model
+- Machine model
+- Machine and assembly language programming
+- Introduction to Quantum Computing
+
+This lecture was held by Prof. Dr. Linnhoff-Popien titled "Rechnerarchitektur" at [https://www.mobile.ifi.lmu.de/](LMU).
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2018-10-15-IOT.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "IoT Practical Exercise"
 categories: teaching
 tags: teaching iot mqtt python influxdb distributed-systems practical-course
@@ -58,4 +59,23 @@ The exercise aimed to solidify theoretical concepts discussed in the main lectur
   This practical work provided direct experience related to the broader lecture themes of IoT connectivity, data handling, and application development.
   </div>
   <div style="clear: both;"></div>
-</div>
+</div>
+=======
+title:  "IOT: Devices & Connectivity"
+categories: teaching
+tags: teaching iot
+excerpt: "Teaching to plan and develope distributed mobile apps for Android as a team."
+header:
+  teaser: assets/images/teaching/server.png
+---
+
+![logo](\assets\images\teaching\server.png){: .align-left style="padding:0.1em; width:5em"}
+In the context of the lecture [Internet of Things (IoT)](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/iot-ws1819/), my task was to come up with a practical exercise which could be implemented in the scope of 1-2 classes. We went with a typical [MQQT](https://mqtt.org/) based communication approach, which incooperated an [InfluxDB](https://www.influxdata.com/) backend, while simulating some high frequency sensors.
+The task was to implement this all from scratch in [Python](https://www.python.org/), which was tought in seperate [lecture](/teaching/python).
+![IOT Influx Pipeline](\assets\figures\iot_inflex_pipeline.png){:style="display:block; margin-left:auto; margin-right:auto; padding: 2em;"}
+This practical course was held in front of about 200 students in winter 2018.
+
+### Contents
+
+The general topics of the lecture included: **1)** Arduino and Raspberry Pi, **2)** Wearables and ubiquitous computing, **3)** Metaheuristics for optimization problems, **4)** Edge/fog/cloud computing and storage, **5)** Scalable algorithms and approaches, **6)** Spatial data mining, **7)** Information retrieval and mining, **8)** Blockchain and digital consensus, **9)** Combinatorial optimization in practice, **10)** Predictive maintenance systems, **11)** Smart IoT applications, **12)** Cyber security & **13)** Web of Things
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2018-10-30-Python.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "Python 101 Course"
 categories: teaching
 tags: teaching python programming introductory-course curriculum-development
@@ -41,4 +42,20 @@ The course structure balanced theoretical instruction with hands-on practical co
   The focus was on providing the essential toolkit for tackling subsequent IoT-related programming tasks.
   </div>
   <div style="clear: both;"></div>
-</div>
+</div>
+=======
+title:  "Lecture: Python 101"
+categories: teaching
+tags: teaching coding 
+excerpt: "Teaching the basics of python."
+header:
+  teaser: assets/images/teaching/py.png
+---
+
+![logo](\assets\images\teaching\py.png){: .align-left style="padding:0.1em; width:5em"}
+The "Python 101"-Lecture was held within the context of the [IOT](/teaching/IOT/) lecture, held in winter semester 2018.
+Over the course of four classes, we tought an extensive introduction to the [`Python`](https://www.python.org/) programming language.
+Not only was the cource slides developed by me and my collegue, we also shared the lectures in front of about 200 students.
+
+There was also a practical part of the course, which allowed students to the practical acquisition of programming skills in the `Python` programming language.
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2019-10-15-operating_systems.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "Operating Systems TA"
 categories: teaching
 excerpt: "TA & Coordinator, Operating Systems lecture (~350 students), system programming."
@@ -46,4 +47,32 @@ The lecture built upon the foundations laid in [Computer Architecture](/teaching
   <div style="clear: both;"></div>
 </div>
 
-  Practical exercises emphasized concurrent programming using Java Threads.
+  Practical exercises emphasized concurrent programming using Java Threads.
+=======
+title:  "Lecture: Operating Systems"
+categories: teaching 
+excerpt: "Teaching the inner working of bits and bytes."
+header:
+  teaser: assets/images/teaching/computer_os.png
+---
+
+![logo](\assets\images\teaching\computer_os.png){: .align-left style="padding:0.1em; width:5em"}In the semesters listed below, my job was to assist in organiszing this bachelors lecture of about 300-400 students.
+We had a team of 10-12 tutors that were employed to balance the workload.
+Also, we created each weeks graded exercise sheets as well as the written exam and organized it.
+
+### Content
+
+<div class="table-right">
+
+| [Winter semester 2019](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/bs-ws1920/)|
+| [Summer semester 2018](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/bs-ws1819/)|
+
+</div>The lecture `Operating Systems` was a continuation of the lecture [`Computer Architecture`](teaching/computer_achitecture/) held in the summer semester.
+The focus of the lecture lay on presenting the concepts of system programming.
+This included the programming of the operating system and of service programs such as editors, compilers and interpreters.
+The lecture provided an overview of the main tasks and problem around operating system, with particular emphasis on the areas of synchronization, process communication, kernel and memory management.
+Java (in particular the Thread API) was used to teach the practical implementation of the concepts introduced in the lecture in the practical exercises.
+At the end of the lecture, the architecture of distributed systems, cross-computer communication and remote procedure calls was discussed, also.
+
+This lecture was held by Prof. Dr. Linnhoff-Popien titled `Betriebssysteme` at [LMU](https://www.mobile.ifi.lmu.de/).
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2020-04-15-IOS.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "iOS App Development"
 categories: teaching
 tags: teaching ios swift mobile-development app-development agile teamwork
@@ -48,4 +49,33 @@ A significant emphasis was placed not only on technical implementation but also
   Students progressed from guided exercises to independent team-based project realization.
   </div>
   <div style="clear: both;"></div>
-</div>
+</div>
+=======
+title:  "IOS - Mobile App Developement"
+categories: teaching
+tags:  app developement
+excerpt: "Teaching to plan and develope distributed mobile apps for IOS as a team."
+header:
+  teaser: assets/images/teaching/ios.png
+---
+
+![logo](\assets\images\teaching\ios.png){: .align-left style="padding:0.1em; width:5em"}
+One semester and with the experience in [andropid app developement](teaching/android), I stepped in to support my collegue in teaching mobile app developement at LMU.
+The lab was divided into two phases:
+**1)** In the introductory phase, the theoretical basics were taught in a weekly preliminary meeting, in addition to practical timeslots.
+**2)** During the project phase, students then worked independently in groups on their own projects.
+There were individual appointments with the project groups to discuss the respective status of the project work.
+
+Specifically, the practical course provided an introduction to programming for the Apple iOS operating system.
+The focus was on programming with Swift and an introduction to specific concepts of programming on mobile devices.
+
+### Content
+
+- Client-Server Architecture
+- Usage of wireless lokal networks (Wifi / Bluetooth)
+- GPS and outdoor positioning
+- Teamwork and planning of timed projects
+- Agile feature developement and tools
+
+IOS app developement was tought as `Praktikum Mobile und Verteilte Systeme (MSP)`
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2022-10-15-android.md

@@ -1,5 +1,6 @@
 ---
 layout: single
+<<<<<<< HEAD
 title: "MSP Android Course"
 categories: teaching
 tags: teaching android java kotlin mobile-development app-development agile teamwork
@@ -55,4 +56,44 @@ Emphasis was placed on applying software engineering best practices within the c
   </ul>
   </div>
   <div style="clear: both;"></div>
-</div>
+</div>
+=======
+title:  "Android - Mobile App Developement"
+categories: teaching 
+tags: app developement  
+excerpt: "Teaching to plan and develope distributed mobile apps for Android as a team."
+header:
+  teaser: assets/images/teaching/android.png
+---
+
+![logo](\assets\images\teaching\android.png){: .align-left style="padding:0.1em; width:5em"}
+Over the course of several semesters me and my collegues tought mobile app developement at [LMU](https://www.mobile.ifi.lmu.de/).
+The lab was divided into two phases:
+**1)** In the introductory phase, the theoretical basics were taught in a weekly preliminary meeting, in addition to practical timeslots.
+**2)** During the project phase, students then worked independently in groups on their own projects.
+There were individual appointments with the project groups to discuss the respective status of the project work.
+
+### Content
+
+<div class="table-right">
+
+| Summer semester | Winter semester |
+|       ---       |       ---       |
+| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-sose22/) | [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-ws2223/)|
+| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-sose21/) | [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-ws2122/)|
+| [2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-sose20/) | [2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-ws2021/)|
+| [2019](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/msp-sose19/)                                    | [2019](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/praktikum-mobile-und-verteilte-systeme-ws1920/)|
+|                                                   ---                                                    | [2018](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/msp-ws1819/)|
+
+</div>
+- Developement of Android-Apps
+- Client-Server Architecture
+- Usage of wireless lokal networks (Wifi / Bluetooth)
+- GPS and outdoor positioning
+- Teamwork and planning of timed projects
+- Agile feature developement and tools
+
+&nbsp;
+
+This course was held as `Praktikum Mobile und Verteilte Systeme (MSP)`
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

_posts/teaching/2023-05-01-seminar-TIMS.md

@@ -0,0 +1,26 @@
+---
+layout: single
+title:  "Seminar: TIMS"
+categories: teaching
+excerpt: "Teaching bachelor students how to work scientifically and how to do research as a team."
+header:
+  teaser: assets/images/teaching/thesis.png
+---
+
+![logo](\assets\images\teaching\thesis.png){: .align-left style="padding:0.1em; width:5em"}
+This seminar deals with selected topics from the field of mobile and distributed systems, in particular from the main research areas of the chair. In recent semesters, this has led to a focus on topics from the field of machine learning and quantum computing.
+
+### Content
+
+<div class="align-right">
+
+| Summer semester | Winter semester |
+|       ---       |       ---       |
+| [2023](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose23/)| --- |
+| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose22/)| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122-2/) |
+| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-sose21/)| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122-2/) |
+| --- |[2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-trends-in-mobilen-und-verteilten-systemen-wise2021/)|
+
+</div>One aim of the seminar is also to learn and practise scientific working techniques. To this end, a course on presentation and working techniques is offered during the semester and supplemented by individual presentation coaching/feedback.
+
+The final grade for the seminar is based on the quality of the academic work, the presentation and active participation in the seminars.

_posts/teaching/2023-05-01-seminar-VTIMS.md

@@ -0,0 +1,26 @@
+---
+layout: single
+title:  "Seminar: VTIMS"
+categories: teaching 
+excerpt: "Teaching master students how to work scientifically and how to do research as a team."
+header:
+  teaser: assets/images/teaching/thesis_master.png
+---
+
+![logo](\assets\images\teaching\thesis_master.png){: .align-left style="padding:0.1em; width:5em"}
+This seminar deals with selected topics from the field of mobile and distributed systems, in particular from the main research topics of the chair.
+In recent semesters, this has led to a focus on topics from the field of machine learning and quantum computing.
+
+### Content
+<div class="table-right">
+
+| Summer semester | Winter semester |
+|       ---       |       ---       |
+| [2023](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose23/)| --- |
+| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose22/)| [2022](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2223/) |
+| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-sose21/)| [2021](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2122/) |
+| --- |[2020](https://www.mobile.ifi.lmu.de/lehrveranstaltungen/seminar-vertiefte-themen-in-mobilen-und-verteilten-systemen-ws2021/)|
+
+</div>One aim of the seminar is also to learn and practise scientific working techniques. To this end, a course on presentation and working techniques is offered during the semester and supplemented by individual presentation coaching/feedback.
+
+The final grade for the seminar is based on the quality of the academic work, the presentation and active participation in the seminars.

blog.md

@@ -1,4 +1,10 @@
 ---
+<<<<<<< HEAD
+=======
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 title: "Blog"
 permalink: /blog/
 layout: category

cv.md

@@ -0,0 +1,9 @@
+---
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+layout: single
+author_profile: true
+title: "Curriculum vitae"
+permalink: /cv
+---

index.md

@@ -1,4 +1,5 @@
 ---
+<<<<<<< HEAD
 layout: home
 author_profile: true
 canonical_url: "https://steffenillium.de"
@@ -18,4 +19,45 @@ This portfolio offers a detailed overview of my academic background, professiona
   <img src="/assets/images/photo/azores.jpg" alt="Stormy coastline of the Azores featuring pink flowers on green grass in the foreground.">
 </figure>
 
-Explore the sections detailing my [research](/research), [teaching](/teaching) experience, key [projects](/projects), and [publications](/publications) to gain deeper insights into my work. You can navigate through the site using the top menu for detailed information on specific areas.
+Explore the sections detailing my [research](/research), [teaching](/teaching) experience, key [projects](/projects), and [publications](/publications) to gain deeper insights into my work. You can navigate through the site using the top menu for detailed information on specific areas.
+=======
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+layout: single
+author_profile: true
+title: "about me"
+canonical_url: "https://steffenillium.de"
+permalink: "/"
+
+---
+
+Hey, glad you found me!
+
+This web page is intended to provide an overview of my current professional life as a doctoral student at the Ludwig Maximilian's University, Munich ([LMU Munich](https://www.lmu.de)).  So, let's get right to the point!
+
+Being at a university means being a teacher in classes, an advisor in practical classes, a speaker, or an organizer in lectures. On the respective pages, you can learn more about my [teaching](teaching) and my [research](research) topics.
+
+Working on projects on behalf of or with partners from industry was another task. Here I learned about audio signal processing and the training of deep neural networks in the context of sequences and image data. In my final year, I was given the opportunity to study multi-agent reinforcement learning in the context of safety and emergent phenomena in fused industrial environments.
+
+Together with my personal interests, this formed the basis for the [publications](publications) we were fortunate enough to work on.
+
+Over the course of my time, my colleagues and I came to work on what were called '*hobbies*', which led me to being the head organizer of a small [open-source conference](https://openmunich.eu).
+Soon thereafter, I took over the editorial office of our [online magazine](https://digitaleweltmagazin.de/). I was fortunate to have the opportunity to work in positionsI had never imagined.
+
+<!-- <figure class="third">
+  <img src="/assets/images/photo/bike.jpg">
+
+  <img src="/assets/images/photo/vulkan_wave.jpg">
+
+  <img src="/assets/images/photo/azores.jpg">
+
+  <img src="/assets/images/photo/sundown.jpg">
+
+  <img src="/assets/images/photo/soft_coral.jpg">
+
+  <img src="/assets/images/photo/natural_pool.jpg">
+</figure> -->
+
+Thank you for coming here :wave:
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

nginx_default.conf

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 # Map to check if client is capable of handling webp
 map $http_accept $webp_suffix {
     default   "";
@@ -58,4 +59,29 @@ server {
     gzip on;
     gzip_comp_level 4;
     gzip_types text/html text/plain text/css application/json application/x-javascript text/xml application/xml application/xml+rss text/javascript;
+=======
+server {
+    listen       80;
+    listen  [::]:80;
+    server_name  localhost;
+
+    #access_log  /var/log/nginx/host.access.log  main;
+
+    location / {
+        root   /usr/share/nginx/html;
+        index  index.html index.htm;
+    }
+
+    error_page  404              /404.html;
+    location = /404.html {
+        root   /usr/share/nginx/html;
+    }
+
+    # redirect server error pages to the static page /50x.html
+    #
+    #error_page   500 502 503 504  /50x.html;
+    #location = /50x.html {
+    #    root   /usr/share/nginx/html;
+    #}
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 }

projects.md

@@ -1,5 +1,12 @@
 ---
+<<<<<<< HEAD
 title: "Projects"
+=======
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+title: "projects"
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 permalink: /projects/
 layout: category
 taxonomy: projects
@@ -7,6 +14,7 @@ author_profile: true
 entries_layout: list
 ---
 
+<<<<<<< HEAD
 This section details key projects undertaken during my tenure as a Research Assistant and PhD candidate at the [Chair for Mobile and Distributed Systems](http://www.mobile.ifi.lmu.de/), LMU Munich (2018-2024), as well as subsequent engagements.
 
 My involvement spanned a diverse range of initiatives, encompassing foundational research, applied projects in collaboration with industry partners such as Stadtwerke München (SWM) and the Fraunhofer Institute for Cognitive Systems (IKS), and significant contributions to academic community organization and outreach. Across these projects, I assumed various responsibilities, including researcher, technical lead, project communicator, conference organizer, and editorial lead.
@@ -15,4 +23,13 @@ The following list provides an overview of these varied engagements, highlightin
 
 ## List of Projects
 
----
+---
+=======
+Here you will find an overview of the projects I worked on at the [mobile and distributed systems chair](http://www.mobile.ifi.lmu.de/).
+I had multiple roles within my time, such as technician, researcher, project communicator, conference organizer, and editor-in-chief.
+Therefore, this list consists of a mix of real industrial projects (in cooperation with SWA and Fraunhofer) and what we call “hobbies” within the chair's reach.
+
+## List of Projects
+
+---
+>>>>>>> d0fa738f (rebase from mnml mistakes source)

publications.md

@@ -1,4 +1,5 @@
 ---
+<<<<<<< HEAD
 title: "Publications"
 permalink: /publications/
 layout: single
@@ -18,6 +19,22 @@ Key areas of investigation reflected in my publications include:
 The publications listed below represent significant outputs from my doctoral studies at LMU Munich and ongoing research activities, contributing to both foundational knowledge and practical solutions.
 
 For a comprehensive and continuously updated list of my publications, please visit my profile on <a href="{{ page.scholar_link }}" target="_blank" rel="noopener noreferrer">Google Scholar</a>.
+=======
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+layout: single
+author_profile: true
+title: "publications"
+permalink: /publications/
+---
+
+This is a list of scientific papers to which I have contributed or which were based on my research and ideas.
+Due to my interest in the general principles of deep learning and neural networks, the topics range from deep dives into the inner workings to real-world applications of neural networks.
+Certainly, the latter were influenced by the [projects](/projects) I was involved in and working on.
+Moreover, my colleagues and I were full of excitement, pursuing rather exotic concepts.
+Please see for yourself. :hugs:
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
  
 ---
 
@@ -39,9 +56,12 @@ For a comprehensive and continuously updated list of my publications, please vis
     <a href="https://www.mobile.ifi.lmu.de/team/steffen-illium/" style="vertical-align:middle">
     <img src="/assets/images/research/lmu.png" style="margin-bottom: 0em;">LMU</a>
 
+<<<<<<< HEAD
     <a href="https://www.semanticscholar.org/author/Steffen-Illium/51893497" style="vertical-align:middle">
     <img src="/assets/images/research/semschol.png" style="margin-bottom: 0em;">Semantic</a>
 
+=======
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
     </figure>
 </center>
 

research.md

@@ -1,4 +1,10 @@
 ---
+<<<<<<< HEAD
+=======
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 #layout: single
 title: "research"
 permalink: /research/
@@ -8,7 +14,12 @@ author_profile: true
 entries_layout: grid
 ---
 
+<<<<<<< HEAD
 Here you'll find a curated overview of the papers where I have played a pivotal role, either as the first author or as a contributing author further down the authorship line. My involvement has spanned a variety of activities, from conceptualizing the initial ideas and developing machine learning models, to providing support and insights to my colleagues, or rigorously reviewing and refining the work.
+=======
+Here you'll find an overview over the papers in which I am listed either as first author or was involved in the process and listed down the line.
+This ranges from the developing the initial idea, to implementing and tuning machine learning models to help my coleagues or simply discussing and checking on a piece of work.
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 
 ## List of Papers
 

teaching.md

@@ -9,9 +9,15 @@ taxonomy: teaching
 author_profile: true
 entries_layout: list
 ---
+<<<<<<< HEAD
 As a doctoral student, embracing the role of an educator brought me great joy, whether it was mentoring undergraduate and graduate students on their theses, assisting in the organization of larger lectures, or leading practical seminars and courses. Below is a list of subjects where I contributed either as an assistant or as the main instructor. 
 
 For a detailed list of thesis topics I have supervised, please visit my [LMU profile page](https://www.mobile.ifi.lmu.de/team/steffen-illium/).
 
+=======
+Being a doctoral student, I was happy to also assume a teaching role, either as a mentor for undergraduate and graduate students' theses, an assistant for arranging larger lectures, or as a facilitator for practical seminars and courses.
+Below, you'll find a list of subjects in which I played an assisting or leading role.
+A comprehensive listing of past thesis topics can be accessed on my [LMU profile page](https://www.mobile.ifi.lmu.de/team/steffen-illium/).
+>>>>>>> d0fa738f (rebase from mnml mistakes source)
 
 ---