base init

.gitignore

@@ -30,3 +30,10 @@ codekit-config.json
 .sass-cache
 _asset_bundler_cache
 _site
+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

Dockerfile

@@ -0,0 +1,3 @@
+FROM nginx:latest
+COPY ./_site/. /usr/share/nginx/html/.
+COPY ./nginx_default.conf /etc/nginx/conf.d/default.conf

Gemfile

@@ -1,2 +1,34 @@
 source "https://rubygems.org"
-gemspec
+# Hello! This is where you manage which Jekyll version is used to run.
+# When you want to use a different version, change it below, save the
+# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
+#
+#     bundle exec jekyll serve
+#
+# This will help ensure the proper Jekyll version is running.
+# Happy Jekylling!
+gem "jekyll"
+gem "minimal-mistakes-jekyll"
+
+# Windows and JRuby does not include zoneinfo files, so bundle the tzinfo-data gem
+# and associated library.
+platforms :mingw, :x64_mingw, :mswin, :jruby do
+  gem "tzinfo", ">= 1", "< 3"
+  gem "tzinfo-data"
+end
+
+# Performance-booster for watching directories on Windows
+gem "wdm", "~> 0.1.1", :platforms => [:mingw, :x64_mingw, :mswin]
+
+# Lock `http_parser.rb` gem to `v0.6.x` on JRuby builds since newer versions of the gem
+# do not have a Java counterpart.
+gem "http_parser.rb", "~> 0.6.0", :platforms => [:jruby]
+
+# My additions
+group :jekyll_plugins do
+  gem 'jekyll-scholar'
+  gem 'jekyll-data'
+  gem 'jemoji'
+  gem 'jekyll-archives'
+  gem 'jekyll-include-cache'
+end

_bibliography.bib

@@ -0,0 +1,215 @@
+---
+---
+References
+==========
+@inproceedings {koelle23primate,
+title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning},
+author = {Kölle, Michael and Illium, Steffen and Zorn, Maximilian and Nü{\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia},
+year = {2023},
+organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023},
+publisher = {Springer CCIS Series},
+}
+
+@inproceedings{zorn23surprise,
+author = {Zorn, Maximilian and Illium, Steffen and Phan, Thomy and Kaiser, Tanja Katharina and Linnhoff-Popien, Claudia and Gabor, Thomas},
+title = {Social Neural Network Soups with Surprise Minimization},
+year = {2023},
+publisher = {MIT Press Direct},
+organization={Conference on Artificial Life - Alife 2023},
+}
+
+@inproceedings{feld2018trajectory,
+  title={Trajectory annotation using sequences of spatial perception},
+  author={Feld, Sebastian and Illium, Steffen and Sedlmeier, Andreas and Belzner, Lenz},
+  booktitle={Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
+  pages={329--338},
+  year={2018}
+}
+
+@article{gabor2022self,
+  title={Self-replication in neural networks},
+  author={Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia},
+  journal={Artificial Life},
+  volume={28},
+  number={2},
+  pages={205--223},
+  year={2022},
+  publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}
+}
+
+@proceedings{gabor2019self,
+    author = {Gabor, Thomas and Illium, Steffen and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia},
+    title = "{Self-Replication in Neural Networks}",
+    volume = {ALIFE 2019: The 2019 Conference on Artificial Life},
+    series = {Artificial Life Conference Proceedings},
+    pages = {424-431},
+    year = {2019},
+    month = {07},
+    doi = {10.1162/isal_a_00197},
+    url = {https://doi.org/10.1162/isal\_a\_00197},
+    eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal2019/31/424/1903421/isal\_a\_00197.pdf},
+}
+
+
+@article{elsner2019deep,
+  title={Deep neural baselines for computational paralinguistics},
+  author={Elsner, Daniel and Langer, Stefan and Ritz, Fabian and Mueller, Robert and Illium, Steffen},
+  journal={arXiv preprint arXiv:1907.02864},
+  year={2019}
+}
+
+@inproceedings{muller2020soccer,
+  title={Soccer Team Vectors},
+  author={Müller, Robert and Langer, Stefan and Ritz, Fabian and Roch, Christoph and Illium, Steffen and Linnhoff-Popien, Claudia},
+  booktitle={Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16--20, 2019, Proceedings, Part II},
+  pages={247--257},
+  year={2020},
+  organization={Springer International Publishing}
+}
+
+@inproceedings{friedrich2020hybrid,
+  title={A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds},
+  author={Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},
+  booktitle={15th International Joint Conference on Computer Graphics Theory and Applications},
+  year={2020}
+}
+
+@inproceedings{sedlmeier2020policy,
+  title={Policy entropy for out-of-distribution classification},
+  author={Sedlmeier, Andreas and Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},
+  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},
+  pages={420--431},
+  year={2020},
+  organization={Springer International Publishing}
+}
+
+@article{muller2020acoustic,
+  title={Acoustic anomaly detection for machine sounds based on image transfer learning},
+  author={Müller, Robert and Ritz, Fabian and Illium, Steffen and Linnhoff-Popien, Claudia},
+  journal={arXiv preprint arXiv:2006.03429},
+  year={2020}
+}
+
+@article{illium2020meantime,
+  title={What to do in the meantime: A service coverage analysis for parked autonomous vehicles},
+  author={Illium, Steffen and Friese, Philipp Andreas and Müller, Robert and Feld, Sebastian},
+  journal={AGILE: GIScience Series},
+  volume={1},
+  pages={7},
+  year={2020},
+  publisher={Copernicus Publications Göttingen, Germany}
+}
+
+@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},
+  year={2020}
+}
+
+@article{muller2020analysis,
+  title={Analysis of feature representations for anomalous sound detection},
+  author={Müller, Robert and Illium, Steffen and Ritz, Fabian and Schmid, Kyrill},
+  journal={arXiv preprint arXiv:2012.06282},
+  year={2020}
+}
+
+@article{muller2021acoustic,
+  title={Acoustic leak detection in water networks},
+  author={Müller, Robert and Illium, Steffen and Ritz, Fabian and Schröder, Tobias and Platschek, Christian and Ochs, Jörg and Linnhoff-Popien, Claudia},
+  journal={arXiv preprint arXiv:2012.06280},
+  year={2020}
+}
+
+@inproceedings{gabor2021goals,
+  title={Goals for self-replicating neural networks},
+  author={Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Linnhoff-Popien, Claudia},
+  booktitle={Artificial Life Conference Proceedings 33},
+  volume={2021},
+  number={1},
+  pages={101},
+  year={2021},
+  organization={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
+}
+
+@article{illium2021visual,
+  title={Visual Transformers for Primates Classification and Covid Detection},
+  author={Illium, Steffen and Müller, Robert and Sedlmeier, Andreas and Popien, Claudia-Linnhoff},
+  journal={Proc. Interspeech 2021},
+  pages={451--455},
+  year={2021}
+}
+
+@article{muller2021deep,
+  title={A Deep and Recurrent Architecture for Primate Vocalization Classification},
+  author={Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},
+  journal={Proc. Interspeech 2021},
+  pages={461--465},
+  year={2021}
+}
+
+@inproceedings{muller2021deep,
+  title={Deep recurrent interpolation networks for anomalous sound detection},
+  author={Müller, Robert and Illium, Steffen and Linnhoff-Popien, Claudia},
+  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
+  pages={1--7},
+  year={2021},
+  organization={IEEE}
+}
+
+@inproceedings{friedrich2022csg,
+  title={CSG Tree Extraction from 3D Point Clouds and Meshes Using a Hybrid Approach},
+  author={Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},
+  booktitle={Computer Vision, Imaging and Computer Graphics Theory and Applications: 15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27--29, 2020, Revised Selected Papers},
+  pages={53--79},
+  year={2022},
+  organization={Springer International Publishing Cham}
+}
+
+@inproceedings{illium2022empirical,
+  title={Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks},
+  author={Illium, Steffen and Schillman, Thore and Müller, Robert and Gabor, Thomas and Linnhoff-Popien, Claudia},
+  booktitle={14th International Conference on Agents and Artificial Intelligence: ICAART},
+  volume={3},
+  number={Proceedings},
+  pages={308--315},
+  year={2022}
+}
+
+@inproceedings{muller2022towards,
+  title={Towards Anomaly Detection in Reinforcement Learning},
+  author={Müller, Robert and Illium, Steffen and Phan, Thomy and Haider, Tom and Linnhoff-Popien, Claudia},
+  booktitle={Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems},
+  pages={1799--1803},
+  year={2022}
+}
+
+@inproceedings{nusslein2022case,
+  title={Case-Based Inverse Reinforcement Learning Using Temporal Coherence},
+  author={Nüßlein, Jonas and Illium, Steffen and Müller, Robert and Gabor, Thomas and Linnhoff-Popien, Claudia},
+  booktitle={Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12--15, 2022, Proceedings},
+  pages={304--317},
+  year={2022},
+  organization={Springer International Publishing Cham}
+}
+
+@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}
+}
+
+@article{kolle2023compression,
+  title={Compression of GPS Trajectories using Autoencoders},
+  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}
+}

_config.yml

@@ -11,76 +11,33 @@
 # https://mmistakes.github.io/minimal-mistakes/docs/quick-start-guide/#installing-the-theme
 
 # theme                  : "minimal-mistakes-jekyll"
+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"
-rtl                      : # true, false (default) # turns direction of the page into right to left for RTL languages
+title                    : "Steffen Illium"
-title                    : "Site Title"
+title_separator          : "---"
-title_separator          : "-"
+subtitle                 : " " # site tagline that appears below site title in masthead
-subtitle                 : # site tagline that appears below site title in masthead
+name                     : Steffen Illium
-name                     : "Your Name"
+description              : "Personal Website"
-description              : "An amazing website."
+url                      : "https://steffenillium.de" # the base hostname & protocol for your site e.g. "https://mmistakes.github.io"
-url                      : # the base hostname & protocol for your site e.g. "https://mmistakes.github.io"
+baseurl                  : "" # the subpath of your site, e.g. "/blog"
-baseurl                  : # the subpath of your site, e.g. "/blog"
+repository               : "illiumst" # GitHub username/repo-name e.g. "mmistakes/minimal-mistakes"
-repository               : # GitHub username/repo-name e.g. "mmistakes/minimal-mistakes"
+teaser                   : "/assets/images/headshot.jpg" # path of fallback teaser image, e.g. "/assets/images/500x300.png"
-teaser                   : # 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           : # overrides the website title displayed in the masthead, use " " for no title
+masthead_title           : "portfolio" # overrides the website title displayed in the masthead, use " " for no title
-breadcrumbs              : # true, false (default)
+breadcrumbs              : true # true, false (default)
 words_per_minute         : 200
-enable_copy_code_button  : # true, false (default)
-copyright                : # "copyright" name, defaults to site.title
-copyright_url            : # "copyright" URL, defaults to site.url
-comments:
-  provider               : # false (default), "disqus", "discourse", "facebook", "staticman", "staticman_v2", "utterances", "giscus", "custom"
-  disqus:
-    shortname            : # https://help.disqus.com/customer/portal/articles/466208-what-s-a-shortname-
-  discourse:
-    server               : # https://meta.discourse.org/t/embedding-discourse-comments-via-javascript/31963 , e.g.: meta.discourse.org
-  facebook:
-    # https://developers.facebook.com/docs/plugins/comments
-    appid                :
-    num_posts            : # 5 (default)
-    colorscheme          : # "light" (default), "dark"
-  utterances:
-    theme                : # "github-light" (default), "github-dark"
-    issue_term           : # "pathname" (default)
-  giscus:
-    repo_id              : # Shown during giscus setup at https://giscus.app
-    category_name        : # Full text name of the category
-    category_id          : # Shown during giscus setup at https://giscus.app
-    discussion_term      : # "pathname" (default), "url", "title", "og:title"
-    reactions_enabled    : # '1' for enabled (default), '0' for disabled
-    theme                : # "light" (default), "dark", "dark_dimmed", "transparent_dark", "preferred_color_scheme"
-    strict               : # 1 for enabled, 0 for disabled (default)
-    input_position       : # "top", "bottom" # The comment input box will be placed above or below the comments
-    emit_metadata        : # 1 for enabled, 0 for disabled (default) # https://github.com/giscus/giscus/blob/main/ADVANCED-USAGE.md#imetadatamessage
-    lang                 : # "en" (default)
-    lazy                 : # true, false # Loading of the comments will be deferred until the user scrolls near the comments container.
-  staticman:
-    branch               : # "master"
-    endpoint             : # "https://{your Staticman v3 API}/v3/entry/github/"
 reCaptcha:
   siteKey                :
   secret                 :
 atom_feed:
   path                   : # blank (default) uses feed.xml
-  hide                   : # true, false (default)
+  hide                   : true # true, false (default)
-search                   : # true, false (default)
+
-search_full_content      : # true, false (default)
-search_provider          : # lunr (default), algolia, google
-lunr:
-  search_within_pages    : # true, false (default)
-algolia:
-  application_id         : # YOUR_APPLICATION_ID
-  index_name             : # YOUR_INDEX_NAME
-  search_only_api_key    : # YOUR_SEARCH_ONLY_API_KEY
-  powered_by             : # true (default), false
-google:
-  search_engine_id       : # YOUR_SEARCH_ENGINE_ID
-  instant_search         : # false (default), true
 # SEO Related
 google_site_verification :
 bing_site_verification   :
@@ -89,76 +46,49 @@ yandex_site_verification :
 baidu_site_verification  :
 
 # Social Sharing
-twitter:
+og_image                 : "/assets/images/headshot.jpg" # Open Graph/Twitter default site image
-  username               :
-facebook:
-  username               :
-  app_id                 :
-  publisher              :
-og_image                 : # 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
-  links: # An array of links to social media profiles
+  links                  : # An array of links to social media profiles
-
+    - https://www.linkedin.com/in/steffen-illium/
-# Analytics
-analytics:
-  provider               : # false (default), "google", "google-universal", "google-gtag", "custom"
-  google:
-    tracking_id          :
-    anonymize_ip         : # true, false (default)
-
 
 # Site Author
 author:
-  name             : "Your Name"
+  name             : "Steffen Illium"
-  avatar           : # path of avatar image, e.g. "/assets/images/bio-photo.jpg"
+  avatar           : "/assets/images/headshot.jpg" # path of avatar image, e.g. "/assets/images/bio-photo.jpg"
-  bio              : "I am an **amazing** person."
+  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         : "Somewhere"
+  location         : "Augsburg"
-  email            :
   links:
-    - label: "Email"
+    - label: "LMU-Munich"
-      icon: "fas fa-fw fa-envelope-square"
+      icon: "fa fa-link"
-      # url: "mailto:your.name@email.com"
+      url: "https://www.mobile.ifi.lmu.de/team/steffen-illium/"
-    - label: "Website"
+    - label: "Scholar"
-      icon: "fas fa-fw fa-link"
+      icon: "ai ai-google-scholar"
-      # url: "https://your-website.com"
+      url: "https://scholar.google.de/citations?hl=en&pli=1&user=NODAd94AAAAJ"
-    - label: "Twitter"
+    - label: "Arxive"
-      icon: "fab fa-fw fa-twitter-square"
+      icon: "ai ai-arxiv"
-      # url: "https://twitter.com/"
+      url: "https://arxiv.org/a/illium_s_1.html"
-    - label: "Facebook"
+    - label: "Researchgate"
-      icon: "fab fa-fw fa-facebook-square"
+      icon: "ai ai-researchgate"
-      # url: "https://facebook.com/"
+      url: "https://www.researchgate.net/profile/Steffen-Illium"
-    - label: "GitHub"
+
-      icon: "fab fa-fw fa-github"
+    
-      # url: "https://github.com/"
-    - label: "Instagram"
-      icon: "fab fa-fw fa-instagram"
-      # url: "https://instagram.com/"
 
 # Site Footer
 footer:
   links:
-    - label: "Twitter"
+    - label: "Email"
-      icon: "fab fa-fw fa-twitter-square"
+      icon: "fas fa-fw fa-envelope"
-      # url:
+      url:  "mailto:steffen.illium@ifi.lmu.de"
-    - label: "Facebook"
+    - label: "LinkedIn"
-      icon: "fab fa-fw fa-facebook-square"
+      icon: "fab fa-fw fa-linkedin"
-      # url:
+      url: "https://www.linkedin.com/in/steffen-illium/"
     - label: "GitHub"
       icon: "fab fa-fw fa-github"
-      # url:
+      url: "https://github.com/illiumst"
-    - label: "GitLab"
-      icon: "fab fa-fw fa-gitlab"
-      # url:
-    - label: "Bitbucket"
-      icon: "fab fa-fw fa-bitbucket"
-      # url:
-    - label: "Instagram"
-      icon: "fab fa-fw fa-instagram"
-      # url:
 
 
 # Reading Files
@@ -192,11 +122,13 @@ exclude:
   - tmp
   - /docs # ignore Minimal Mistakes /docs
   - /test # ignore Minimal Mistakes /test
+  - Dockerfile
+  - nginx_default.conf
 keep_files:
   - .git
   - .svn
 encoding: "utf-8"
-markdown_ext: "markdown,mkdown,mkdn,mkd,md"
+# markdown_ext: "markdown,mkdown,mkdn,mkd,md"
 
 
 # Conversion
@@ -204,7 +136,7 @@ markdown: kramdown
 highlighter: rouge
 lsi: false
 excerpt_separator: "\n\n"
-incremental: false
+incremental: true
 
 
 # Markdown Processing
@@ -217,6 +149,7 @@ kramdown:
   toc_levels: 1..6
   smart_quotes: lsquo,rsquo,ldquo,rdquo
   enable_coderay: false
+  parse_block_html: true
 
 
 # Sass/SCSS
@@ -263,6 +196,10 @@ plugins:
   - jekyll-gist
   - jekyll-feed
   - jekyll-include-cache
+  - jekyll-scholar
+  - jekyll-data
+  - jekyll-archives
+  - jemoji
 
 # mimic GitHub Pages with --safe
 whitelist:
@@ -270,7 +207,7 @@ whitelist:
   - jekyll-sitemap
   - jekyll-gist
   - jekyll-feed
-  - jekyll-include-cache
+   jekyll-include-cache
 
 
 # Archives
@@ -283,23 +220,23 @@ whitelist:
 #  - <base_path>/tags/my-awesome-tag/index.html ~> path: /tags/
 #  - <base_path>/categories/my-awesome-category/index.html ~> path: /categories/
 #  - <base_path>/my-awesome-category/index.html ~> path: /
-category_archive:
+# category_archive:
-  type: liquid
+#   type:  jekyll-archives
-  path: /categories/
+#   path: /categories/
 tag_archive:
-  type: liquid
+  type:  jekyll-archives
   path: /tags/
 # https://github.com/jekyll/jekyll-archives
-# jekyll-archives:
+jekyll-archives:
-#   enabled:
+  enabled:
-#     - categories
+    # - categories
-#     - tags
+    - tags
-#   layouts:
+  layouts:
-#     category: archive-taxonomy
+    category: archive-taxonomy
-#     tag: archive-taxonomy
+    tag: archive-taxonomy
-#   permalinks:
+  permalinks:
-#     category: /categories/:name/
+    category: /:name/
-#     tag: /tags/:name/
+    tag: /tags/:name/
 
 
 # HTML Compression
@@ -319,7 +256,18 @@ defaults:
     values:
       layout: single
       author_profile: true
-      read_time: true
+      read_time: false
-      comments: # true
+      comments: false
-      share: true
+      share: false
-      related: true
+      related: false
+      show_date: true
+
+scholar:
+  style: modern-language-association
+  bibliography: _bibliography.bib
+  source: ""
+  sort_by: year,month
+  order: descending
+  group_by: year
+  group_order: descending
+  relative: "/publications"

_data/navigation.yml

@@ -1,12 +1,13 @@
-# main links
 main:
-  - title: "Quick-Start Guide"
+  - title: "teaching"
-    url: https://mmistakes.github.io/minimal-mistakes/docs/quick-start-guide/
+    url: /teaching
-  # - title: "About"
+  #- title: "Projects"
-  #   url: https://mmistakes.github.io/minimal-mistakes/about/
+  #  url: /projects
-  # - title: "Sample Posts"
+  - title: "research"
-  #   url: /year-archive/
+    url: /research
-  # - title: "Sample Collections"
+  # - title: "Blog"
-  #   url: /collection-archive/
+  #   url: /blog
-  # - title: "Sitemap"
+  - title: "publications"
-  #   url: /sitemap/
+    url: /publications
+  #- title: "CV"
+  #  url: /cv

_posts/blog/2022-01-22-wireguard-ui-nat-vps.md

@@ -0,0 +1,9 @@
+---
+layout: single
+title:  "Welcome to Jekyll!"
+categories: blog
+excerpt: "A unique line of text to describe this post that will display in an archive listing and meta description with SEO benefits."
+
+---
+
+W. I. P.

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

@@ -0,0 +1,13 @@
+---
+layout: single
+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"}
+
+

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

@@ -0,0 +1,11 @@
+---
+layout: single
+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"}

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

@@ -0,0 +1,12 @@
+---
+layout: single
+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"}

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

@@ -0,0 +1,11 @@
+---
+layout: single
+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"}

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

@@ -0,0 +1,11 @@
+---
+layout: single
+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"}

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

@@ -0,0 +1,9 @@
+---
+layout: single
+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"}

_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

@@ -0,0 +1,13 @@
+---
+layout: single
+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;"}

_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

@@ -0,0 +1,34 @@
+---
+layout: single
+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).

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

@@ -0,0 +1,19 @@
+---
+layout: single
+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

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

@@ -0,0 +1,16 @@
+---
+layout: single
+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.

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

@@ -0,0 +1,28 @@
+---
+layout: single
+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/).

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

@@ -0,0 +1,29 @@
+---
+layout: single
+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)`

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

@@ -0,0 +1,40 @@
+---
+layout: single
+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)`

_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.