Index

Design eines Systems zur Textklassifikation von Bounce-E-Mails im E-Rechnung Dienst

EcoScapes: LLM-powered advice for crafting sustainable cities

Climate adaptation is vital for the sustainability and sometimes the mere survival of our urban
areas [1, chapters TS.C.8 and TS.D.1]. However, small cities often struggle with limited personnel
resources and integrating vast amounts of data from multiple sources for a comprehensive analysis
[1, chapter TS.D.1.3]. Moreover, the complexity of the topic can overwhelm administrative staff and
local politicians alike. To overcome these challenges, this thesis proposes a multi-layered system
combining specialized Large Language Models (LLMs), satellite imagery and a knowledge base to aid
in developing effective climate adaptation strategies.
Initially, the system uses provided location information to request relevant satellite imagery, which can
be used by all subsequent components.
The architecture’s modular core encompasses several LLMs and expert systems that examine the
satellite data to offer insights on different climate adaptation aspects. Examples of potential functions
might include, but are not limited to, the identification of heat islands, areas threatened by flooding, or
the assessment of vegetation cover.
In the last step, the system consolidates the findings from the preceding modules to generate a
comprehensive report on the existing situation and recommend potential adaptation strategies.
In order to assess the system’s performance, we will compare the generated outputs with those of
unaltered LLMs and a model inspired by ChatClimate [2].

References

[1] P¨ortner, H.-O., D.C. Roberts, H. Adams. et al. 2022: Technical Summary. [H.-O. P¨ortner, D.C. Roberts,
E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr´ıa, M. Craig, S. Langsdorf, S. L¨oschke, V. M¨oller,
A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of
Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
[H.-O. P¨ortner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr´ıa, M. Craig, S.
Langsdorf, S. L¨oschke, V. M¨oller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge,
UK and New York, NY, USA, pp. 37-118, doi:10.1017/9781009325844.002.

[2] Vaghefi, S.A., Stammbach, D., Muccione, V. et al. ChatClimate: Grounding conversational AI in climate
science. Commun Earth Environ 4, 480 (2023). https://doi.org/10.1038/s43247-023-01084-x

Verification of deep learning classifications in test systems for industrial productions

Analyzing the influence of writer-depended features in writer identification using Convolutional Neural Networks

Feature Extraction and Dimensionality Reduction Techniques for Assessing Similarity in Large-Scale 3D CAD Datasets

Work description
The research presented in this thesis explores the application of feature extraction and dimensionality reduction techniques to assess model similarity within large-scale 3D CAD datasets. It investigates how different geometric and topological descriptors can be quantified and utilized to measure the similarity between complex 3D models. Therefore, the study employs advanced machine learning algorithms to analyze and cluster 3D data, facilitating a better understanding of model characteristics and relationships.

During the thesis, the following questions should be considered:

What metrics can effectively quantify the variance in a training dataset?
How does the variance within a training set impact the neural network’s ability to generalize to new, unseen data?
What is the optimal balance of diversity and specificity in a training dataset to maximize NN performance?
How can training datasets be curated to include a beneficial level of variance without compromising the quality of the neural network’s output?
What methodologies can be implemented to systematically adjust the variance in training data and evaluate its impact on NN generalization?

Prerequisites
Applicants should have a solid background in machine learning and deep learning, with strong technical skills in Python and experience with PyTorch. Candidates should also possess the capability to work independently and have a keen interest in exploring the theoretical aspects of neural network training.

For your application, please send your transcript of record.

Definition und Implementierung einer prototypischen Smart Home Schnittstelle für ein cloudbasiertes Energiemanagementsystem

Transformers vs. Convolutional Networks for 3D segmentation in industrial CT data

The current state of the art for segmentation in industrial CT are oftentimes CNNs.
Transformer based models are sparsely used.
Therefore, this project wants to compare the semantic segmentation performance of transformers (that include global context into segmentation), pure convolutional neural networks (that use local context) and combined methods (like this one: https://doi.org/10.1186/s12911-023-02129-z) on an industrial CT dataset of shoes like in this study: https://doi.org/10.58286/27736 .

Only available as Bachelors thesis / Research Project

Developing and Evaluating Image Similarity Metrics for Enhanced Classification Performance in 2D Datasets

Work description
This thesis focuses on the development and evaluation of novel image similarity metrics tailored for 2D datasets, aiming to improve the effectiveness of classification algorithms. By integrating active learning methods, the research seeks to refine these metrics dynamically through iterative feedback and validation. The work involves extensive testing and validation across diverse 2D image datasets, ensuring robustness and applicability in varied scenarios.

The following questions should be considered:

What metrics can effectively quantify the variance in a training dataset?
How does the variance within a training set impact the neural network’s ability to generalize to new, unseen data?
What is the optimal balance of diversity and specificity in a training dataset to maximize NN performance?
How can training datasets be curated to include a beneficial level of variance without compromising the quality of the neural network’s output?
What methodologies can be implemented to systematically adjust the variance in training data and evaluate its impact on NN generalization?

For your application, please send your transcript of record.