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The emergence of advanced reasoning capabilities in Large Language Models (LLMs) marks a transformative development in healthcare applications. Beyond merely expanding functional capabilities, these reasoning mechanisms enhance decision transparency and explainability-critical requirements in medical contexts. This survey examines the transformation of medical LLMs from basic information retrieval tools to sophisticated clinical reasoning systems capable of supporting complex healthcare decisions. We provide a thorough analysis of the enabling technological foundations, with a particular focus on specialized prompting techniques like Chain-of-Thought and recent breakthroughs in Reinforcement Learning exemplified by DeepSeek-R1. Our investigation evaluates purpose-built medical frameworks while also examining emerging paradigms such as multi-agent collaborative systems and innovative prompting architectures. The survey critically assesses current evaluation methodologies for medical validation and addresses persistent challenges in field interpretation limitations, bias mitigation strategies, patient safety frameworks, and integration of multimodal clinical data. Through this survey, we seek to establish a roadmap for developing reliable LLMs that can serve as effective partners in clinical practice and medical research.

The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regulations, while preserving data usability for diverse and crucial downstream tasks. This survey provides a comprehen-sive overview of current trends and recent advances in text anonymization techniques. We begin by discussing foundational approaches, primarily centered on Named Entity Recognition, before examining the transformative impact of Large Language Models, detailing their dual role as sophisticated anonymizers and potent de-anonymization threats. The survey further ex-plores domain-specific challenges and tailored solutions in critical sectors such as healthcare, law, finance, and education. We investigate advanced methodologies incorporating formal privacy models and risk-aware frameworks, and address the specialized subfield of authorship anonymization. Additionally, we review evaluation frameworks, comprehensive metrics, benchmarks, and practical toolkits for real-world deployment of anonymization solutions. This review consolidates current knowledge, identifies emerging trends and persistent challenges, including the evolving privacy-utility trade-off, the need to address quasi-identifiers, and the implications of LLM capabilities, and aims to guide future research directions for both academics and practitioners in this field.

We propose STONK (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical & textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows STONK outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configurations offers evidence-based guidance for scalable multimodal financial forecasting. Source code is available on GitHub11https://github.com/sarthak-12/thesis-dsaa/.

While large language models (LLMs) have demonstrated impressive results on public benchmarks, their effectiveness in structured, real-world problems like behavioral analytics remains underexplored. This work assesses the out-of-the-box performance of LLMs for industry-specific downstream tasks, with player churn prediction as a representative task. Evaluating LLMs on public benchmarks risks data leakage and task-specific overfitting, so instead we perform experiments on a novel self-compiled dataset for churn prediction, a task not part of any standard benchmark. We compare the performance of OpenAI's GPT-4.1 with traditional machine learning models, such as XGBoost and MLPs, and analyze the impact of the LLM's extensive internal world knowledge and reasoning capabilities. With few-shot prompting, GPT-4.1 achieves a weighted F1 score of 0.787, matching the performance of XGBoost on the same set of samples. We show that the LLM can compensate for missing information with its internal world knowledge and reasoning capabilities, performing best if it can leverage both. Our results highlight the potential of LLMs for cross-game churn prediction and other structured, industry-specific tasks.

Large Language Models (LLMs) have demonstrated remarkable capabilities but their significant computational and memory demands hinder widespread deployment, especially on resource-constrained devices. Quantization, the process of reducing the numerical precision of model parameters, has emerged as a critical technique for compressing LLMs and accelerating inference. This paper provides an overview of LLM quantization, with a particular focus on the Post-Training Quantization (PTQ) methods implemented within the popular llama. cpp framework and its GGUF file format. We begin by covering quantization fundamentals, including the distinction between PTQ and Quantization-Aware Training (QAT). We then describe the specific PTQ schemes employed by llama. cpp, including legacy methods, advanced K-quants, and recent IQ-quants, along with their underlying mathematical principles. The paper also discusses the impact of these techniques on model fidelity, hardware requirements, inference speed, and traces the adoption of GGUF as a de facto standard in the open-source community. This work serves as a practical guide and comprehensive reference for researchers aiming to deploy LLMs on resource-constrained hardware. By systematically documenting and comparing the PTQ methods within llama. cpp, we provide the necessary insights to navigate the trade-offs between model fidelity, inference speed, and memory footprint. This enables informed decision-making for real-world applications, from local CPU-based inference to efficient edge deployment.

Surgical gauze is an essential part of surgical procedures, which is primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical retention of gauze can lead to serious complications in the patient's health and necessitate additional surgery for gauze removal. In the wake of data scarcity, the research on gauze segmentation on the real-world surgical data remains underexplored. In this work, we investigate the use of deep learning methods for gauze segmentation in robotassisted minimally invasive abdominal surgeries, utilizing an inhouse surgical dataset prepared at a university hospital. The training data reflects a realistic surgical setting and extensive diversity in spatial, morphological, and visual attributes of three different gauze categories. We have investigated prevalently used segmentation architectures, including CNN-based, transformer-based, and hybrid architectures, to provide a proof-of-concept for gauze segmentation in a realistic setting. Besides, we investigate the influence of additional sub-optimally annotated, auto-tracked segmentation masks to address the bottleneck of data scarcity and performance optimization. Our results demonstrate the efficacy of real-world data to counter the main challenge reported by prior works - the trade-off between blood presence and gauze detection. The incorporation of auto-track annotations enables performance enhancements, particularly in generic cases. The integration of effective segmentation approaches will benefit robotguided surgical procedures and various downstream applications by providing a precise delineation of foreign objects, enhancing patient safety and surgical outcomes.

Despite Arabic being one of the most widely spoken languages, there is a scarcity of available dialectal Arabic data. In this paper, we address this challenge by proposing a novel approach to data collection through the main use of video captions from TikTok, and other resources such as dictionaries and articles, resulting in the creation of the ArDia dataset. To the best of our knowledge, the ArDia dataset is the largest labeled dialectal Arabic dataset, containing over 900,000 examples, each labeled with its respective dialect. We further leverage this dataset to pretrain transformer-based models, ArDiaBERT and ArDiaGPT. Due to a lack of research on the Arabic models, we present a comprehensive study of Arabic dialect identification using the ArDia dataset on the dialect identification task.

The auditing of financial documents, traditionally a labor-intensive task, is a promising field of application for Artificial Intelligence. Recommendation systems are capable of suggesting the most relevant passages from financial reports that meet accounting standards’ legal requirements. However, testing if the compliance requirements are satisfied is a non-trivial task. In this work, we tackle this problem from two directions. Our first approach leverages Large Language Models which we fine-tune specifically f or compliance checks. Our results show an improvement in performance over the generic baseline LLMs. A disadvantage of LLMs is that they result in high inference costs. For this reason, we explore a second approach in which we use smaller models that come with reduced running costs. Despite their smaller size, these models also show promising predictive performance.

This study explores efficient algorithms to enhance user matching in Unrare.me, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform.

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from Unrare.me consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36%$(\pm 2.03)$, surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges.

With over 7,000 known rare diseases and a prevalence of less than one in a thousand, rare diseases pose substantial challenges to advanced medical support networks. This study investigates the efficacy of Unrare.me, a novel social networking platform designed for individuals affected by rare diseases, including patients, their family members, and medical professionals, addressing data sparsity and combinatorial complexities in user matching. We demonstrate that simple matching heuristics already serve as a decent basis for collecting user feedback on match quality. Leveraging over 10,000 user matching feedback scores from more than 2,000 active users, we evaluate algorithms including collaborative filtering and user embedding similarity with state-of-the-art Large Language Models (LLMs). With a top-10 and top-5 hit-rate of 55% and 37%, respectively, we show that a combination of medical data augmentation and embeddings significantly enhances performance beyond the initial heuristic baseline.

Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition are still largely unaffected by this progress as they are primarily based on the previous generation of encoder-only transformer models. Here, we propose a simple yet effective approach, Informed Named Entity Recognition Decoding (iNERD), which treats named entity recognition as a generative process. It leverages the language understanding capabilities of recent generative models in a future-proof manner and employs an informed decoding scheme incorporating the restricted nature of information extraction into open-ended text generation, improving performance and efficiency and eliminating any risk of hallucinations. We coarse-tune our model on a merged named entity corpus to strengthen its performance, evaluate five generative language models on eight named entity recognition datasets, and achieve remarkable results, especially in an environment with an unknown entity class set, demonstrating the adaptability of the approach.

Accurate detection of dementia is crucial for timely intervention and care, and leveraging multimodal data holds significant potential for improving diagnostic accuracy. In this study, we explore deep learning approaches for dementia classification using the Pitt corpus, which includes brief participant descriptions of a cookie theft scene. We analyze 242 control and 307 dementia audio clips to investigate various representation learning techniques. Our best-performing approach fuses audio spectrograms with advanced language models, including Whisper model transcriptions and transformer-based feature extraction. We rigorously evaluate these models and find that our multimodal approach with an F1-score of 86.42% eclipses other single modality approaches by a considerable margin. Our findings underscore the promise of multimodal deep learning techniques in advancing the reliability of dementia detection through audio analysis, possibly paving the way for more robust and accessible diagnostic tools.

Named Entity Recognition (NER) in the legal domain presents unique challenges due to specialized terminology and complex linguistic structures inherent in legal texts. While large language models (LLMs) like GPT-4, Llama-3, and others have significantly advanced natural language processing, their effectiveness in domain-specific tasks like legal Named Entity Recognition remains underexplored. This study conducts a comprehensive comparative analysis of eleven state-of-the-art LLMs on legal NER tasks across seven diverse datasets in five languages, namely English, Portuguese, German, Turkish, and Ukrainian. We evaluate the models’ performance using F1scores, focusing on their ability to accurately identify and classify legal entities. Our findings reveal significant variability in LLM performance across different languages and legal contexts, with proprietary models like GPT-4 achieving the highest overall scores. The results highlight the influence of model architecture, dataset characteristics, and prompt design on the effectiveness of legal NER tasks. This study provides valuable benchmarks for legal NER applications and offers insights into the strengths and limitations of current LLMs, guiding future research and development in legal natural language processing.

We explore the use of Large Language Models (LLMs) for automating the extraction of Key Performance Indicators (KPIs) from diverse financial reports without any additional fine-tuning. We focus on evaluating various proprietary and open-source LLMs to address the joint named entity recognition and relation extraction tasks essential for accurately linking KPIs to their corresponding values and attributes. Our study highlights the technical challenges involved in the extraction process and presents a comprehensive evaluation of the models’ effectiveness. Our results reveal significant insights into handling these LLMs in such a crucial environment and showcase the transformative potential of LLMs in enhancing financial analysis and decision-making.

Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability. However, due to the fact that they can only manipulate input to see changes in output, they suffer from weak performance when used with complex model architectures. For models with large inputs as, for instance, in object detection, sampling-based methods like KernelSHAP are inefficient due to many computation-heavy forward passes through the model. In this work, we present a framework for using sampling-based explanation methods in a computer vision context shown for body part relevance assessment for pedestrian detection. Furthermore, we introduce a novel sampling-based method similar to KernelSHAP that shows more robustness for lower sampling sizes and, thus, is more efficient for explainability analyses on large-scale datasets. We demonstrate our relevance assessment method on simulation data acquired with the CARLA simulator. In the end, our method enables the benchmarking and performance comparison of various pedestrian detection models based on human-interpretable semantic regions.

Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.

We perform a linguistic investigation of causality in financial reports, and find that there are a number of intricacies specific to this domain, making it hard for a machine learning model to accurately detect causal statements. Specifically, cause and effect clauses are oftentimes very subtle or implicit. Additionally, some degree of world knowledge and reasoning is necessary to successfully identify many of those statements. We apply our findings by prompting GPT-4o with the acquired knowledge to improve its predictive capabilities. The results suggest that an informed approach can help enhance the performance of a causality detection system, possibly allowing for more intelligent and light-weight solutions in the future.

We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed framework offers significant advantages over the current approach, which encompasses only qualitative collection of survey data or a static survey with limited answer options. Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment. Based on this input, we will use machine learning to infer conventional PROM scores, which can be used by clinicians to evaluate the treatment status. The goal of the application is to improve adherence to the healthcare system and treatments, and thus ultimately reduce cases of subsequent vision impairment. The approach needs to be further validated using a survey and a clinical study.

The ability of large language models (LLMs) to simulate human behavior and psychological traits holds significant promise for applications in psychology and the social sciences. This paper investigates the feasibility of generating synthetic dialogue datasets that accurately reflect real-world distributions of personality traits, based on the Big Five personality model. Using GPT-4o-mini, we prompt the model with personality traits that mirror population-level distributions to generate dialogues. However, systematic deviations, particularly in the representation of extreme personality traits, are observed — likely due to biases introduced during LLM training and alignment. To address these deviations, we propose a rescaling method that corrects the initial personality traits used for prompting the LLM, ensuring that the generated dialogues more closely match the expected distributions. This correction enhances the quality and reliability of the synthetic dialogues, paving the way for more effective use of LLMs in psychological research and social science applications.

Table structure recognition (TSR), the task of inferring the layout of tables, including the row, column, and cell structure, is a surprisingly complex task. With the growing amount and importance of digital documents, it has become an increasingly relevant problem, which nonetheless has not yet been solved adequately and still presents a very active area of research. In recent years, a growing number of deep-learning-based approaches to table parsing have been proposed.This paper presents a novel deep-learning-based table structure recognition method that can predict row, column, and cell bounds for table images with a high degree of accuracy. To achieve this goal, a multi-stage pipeline incorporating a Vision-Transformer-based Autoencoder model was devised. This model was trained to predict cell regions for table images, from which accurate cell bounds can be inferred, including spanning cells which cover multiple rows or columns. The goal was to obtain a model that generalizes well and can return accurate predictions on various tables of differing complexity, even if they contain little initial structural information.An additional modification to the model architecture presented in the Masked Autoencoder (MAE) approach was also evaluated.

The auditing of financial documents, historically a labor-intensive process, stands on the precipice of transformation. AI-driven solutions have made inroads into streamlining this process by recommending pertinent text passages from financial reports to align with the legal requirements of accounting standards. However, a glaring limitation remains: these systems commonly fall short in verifying if the recommended excerpts indeed comply with the specific legal mandates. Hence, in this paper, we probe the efficiency of publicly available Large Language Models (LLMs) in the realm of regulatory compliance across different model configurations. We place particular emphasis on comparing cutting-edge open-source LLMs, such as Llama-2, with their proprietary counterparts like OpenAI’s GPT models. This comparative analysis leverages two custom datasets provided by our partner PricewaterhouseCoopers (PwC) Germany. We find that the open-source Llama-2 70 billion model demonstrates outstanding performance in detecting non-compliance or true negative occurrences, beating all their proprietary counterparts. Nevertheless, proprietary models such as GPT-4 perform the best in a broad variety of scenarios, particularly in non-English contexts.

Correct identification and correction of contradictions and inconsistencies within financial reports constitute a fundamental component of the audit process. To streamline and automate this critical task, we introduce a novel approach leveraging large language models and an embedding-based paragraph clustering methodology. This paper assesses our approach across three distinct datasets, including two annotated datasets and one unannotated dataset, all within a zero-shot framework. Our findings reveal highly promising results that significantly enhance the effectiveness and efficiency of the auditing process, ultimately reducing the time required for a thorough and reliable financial report audit.

Understanding the dynamics of blood flow is crucial in the context of cardiovascular health and disease. The dynamics of the blood flow can be a significant parameter for the development of decision support systems to enable early detection and accurate diagnosis of coronary artery diseases. Uncovering the underlying dynamics from high-dimensional data generated from experiments is a highly complex problem at the intersection of artificial intelligence and applied mathematics. Deep Hidden Physics Models can be used to learn the underlying dynamics without additional physical knowledge.In this work, the potential of Deep Hidden Physics Models to model the clinically relevant dynamics of blood flow is investigated. The experiments consider the use case of stenosis in two-dimensional spatial space. Based on the learned dynamics, the concentration field can be approximated accurately, indicating that the dynamics are learned correctly. Additionally, we examine the capability of the model to extrapolate the learned dynamics for unknown time intervals.

We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules. Our vision is to provide a condensed corpus of prototypical contradictions, allowing for in-depth linguistic analysis as well as efficient language model fine-tuning. To this end, we instruct the generative models to create contradicting statements with respect to descriptions of specific contradiction types. In addition, the model is also instructed to come up with completely new contradiction typologies. As an auxiliary approach, we use linguistic rules to construct simple contradictions such as those arising from negation, antonymy and numeric mismatch. We find that our methods yield promising results in terms of coherence and variety of the data. Further studies, as well as manual refinement are necessary to make use of this data in a machine learning setup.

Deep Learning is proving its immense potential in medical image processing. However, noisy label data can weaken the generalization ability of the model and cause significant performance degradation. This issue can be more prevalent in multi-label classification tasks, since their annotation is more challenging. With too many labels, human annotators may have difficulties mentioning all possible classes, which leads to an increased number of false negative labels. Single Positive Multi-Label (SPML) training deals with the most severe version of this problem, in which for each sample only one positive label is available. All other labels are not observed, i.e. not confirmed as positive or negative. While SPML has already achieved good results for the multi-label detection of objects, its impact on the extremely pertinent medical imaging use case has not yet been explored. In this work, we therefore investigate the performance of state-of-the-art SPML loss functions in the analysis of chest X-rays, both on the public CheXpert and on an in-house data set of the University Hospital Bonn. In addition, we propose our new SPML loss functions, the Generalized Assume Negative and Implicit Weighting Assume Negative loss, which increase the mean average precision by up to 4.6% compared to the popular binary cross-entropy loss.

In this work, we investigate the player behavior of an NFT-based blockchain game from a perspective of attrition with the goal of providing insights as to how players interact with the game over a long period of time and establishing potential avenues for future research in building prediction models. To this end, we analyze the retention behavior over time and profile the players to gain insights about how they interact with the game.

Previous research in behavioral- and game analytics showed that data augmentation plays a crucial role against the challenges of detecting minority entities (e.g. premium or retaining users) in behavioral datasets. By putting more emphasis on the minority entities, data augmentation allows us to utilize existing solutions without the need for extensive adjustments. In this study, we build upon previous work in this area by providing a comparison from both a methodology perspective and a data alteration perspective. The comparison focuses on three methods: Synthetic Minority Oversampling Technique (a nearest neighbor based approach), Variational Autoencoders, and Generative Adversarial Networks (both deep learning based approaches). We conduct an empirical evaluation using retention prediction in a freemium mobile game. Our findings indicate that each method offers advantages in terms of improved generalization results for different evaluation measures.

Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.

We introduce KPI-Check, a novel system that automatically identifies and cross-checks semantically equivalent key performance indicators (KPIs), e.g. "revenue" or "total costs", in real-world German financial reports. It combines a financial named entity and relation extraction module with a BERT-based filtering and text pair classification component to extract KPIs from unstructured sentences before linking them to synonymous occurrences in the balance sheet and profit & loss statement. The tool achieves a high matching performance of 73.00% micro F1on a hold out test set and is currently being deployed for a globally operating major auditing firm to assist the auditing procedure of financial statements.

We consider Automated List Inspection (ALI), a content-based text recommendation system that assists auditors in matching relevant text passages from notes in financial statements to specific law regulations. ALI follows a ranking paradigm in which a fixed number of requirements per textual passage are shown to the user. Despite achieving impressive ranking performance, the user experience can still be improved by showing a dynamic number of recommendations. Besides, existing models rely on a feature-based language model that needs to be pre-trained on a large corpus of domain-specific datasets. Moreover, they cannot be trained in an end-to-end fashion by jointly optimizing with language model parameters. In this work, we alleviate these concerns by considering a multi-label classification approach that predicts dynamic requirement sequences. We base our model on pre-trained BERT that allows us to fine-tune the whole model in an end-to-end fashion, thereby avoiding the need for training a language representation model. We conclude by presenting a detailed evaluation of the proposed model on two German financial datasets.

One-vs-Rest (OVR) classification aims to distinguish a single class of interest from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class extends from the classes observed during training to unseen and possibly unrelated classes. In this work, we propose a novel architecture, namely Decoupling Autoencoder (DAE) to tackle the common issue of robustness w.r.t. out-of-distribution samples which is prevalent in classifiers such as multi-layer perceptrons (MLP) and ensemble architectures. Experiments on plain classification, outlier detection, and dataset shift tasks show DAE to achieve robust performance across these tasks compared to the baselines, which tend to fail completely, when exposed to dataset shift. While DAE and the baselines yield rather uncalibrated predictions on the outlier detection and dataset shift task, we found that DAE calibration is more stable across all tasks. Therefore, calibration measures applied to the classification task could also improve the calibration of the outlier detection and dataset shift scenarios for DAE.

Natural Language Processing, and specifically Sentiment Analysis are still unexplored topics with respect to Turkish text. A key challenge is to extract meaningful word and paragraph representations. We provide a comprehensive overview on pre-processing and featurization methods for this problem. Our focus is on the inherent difficulties that come with analyzing Turkish real-world data from the e-commerce domain, such as inconsistent spelling or complicated morphological and grammatical structures.

In this paper we introduce a projected gradient descent algorithm to find kernel minimum enclosing balls and compare it to a gradient based Frank-Wolfe algorithm. We base our comparison on empirical as well as theoretical observations of the two methods by comparing different aspects of their behaviors that involve runtime, stability, abilities to find novel datapoints as well as convergence rates.

In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems.

The combinatorial problem of max-sum diversification asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, max-sum diversification can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might play a role in data mining. We therefore discuss quantum computing in a tutorial like manner and elaborate on its current strengths and weaknesses for data analysis.

We present a tool that enables benchmarking of companies by means of automatic extraction of key performance indicators from publicly available financial reports. Our tool monitors companies of interest so that their reports are automatically downloaded as soon as they become available. After tables and paragraphs have been extracted from the documents using a table detection module based on convolutional neural networks, relevant key performance indicators are stored in a central database. The extracted values are finally displayed in a user-friendly web application where the user can compare time series of key performance indicators against arbitrary available companies.

We present the Automated List Inspection (ALI) tool that utilizes methods from machine learning, natural language processing, combined with domain expert knowledge to automate financial statement auditing. ALI is a content based context-aware recommender system, that matches relevant text passages from the notes to the financial statement to specific law regulations. In this paper, we present the architecture of the recommender tool which includes text mining, language modeling, unsupervised and supervised methods that range from binary classification models to deep recurrent neural networks. Next to our main findings, we present quantitative and qualitative comparisons of the algorithms as well as concepts for how to further extend the functionality of the tool.

We present a simple, two step procedure that selects central and extremal prototypes from a given set of data. The key idea is to identify minima of the function that characterizes the interior of a kernel minimum enclosing ball of the data. We discuss how to efficiently compute kernel minimim enclosing balls using the Frank-Wolfe algorithm and show that, for Gaussian kernels, the sought after prototypes can be naturally found via a variant of the mean shift procedure. Practical results demonstrate that prototypes found this way are descriptive, meaningful, and interpretable.

We present an online learning algorithm to extract extremal prototypes from a set of data. As an online algorithm, our method can continue to learn during the application phase of a system. However, as a greedy update procedure, it may be sensitive to outliers. We therefore consider the use of extreme value theory for self-assessment and discuss how to incorporate Weibull statistics so as to increase robustness. We evaluate our approaches on synthetic as well as real world datasets to perform profiling. Our empirical results show that incorporating self-assessment not only results in better data representations but also reveals interpretable insights about the analyzed dataset.