In the International Future Lab for Artificial Intelligence (Leibniz AI Lab) in Hannover, excellent international researchers as well as renowned colleagues from L3S Research Center, Leibniz University, Hannover Medical School and European partner institutes have been researching new topics in artificial intelligence and developing intelligent solutions for personalised medicine.

• SelfDistill-SER: Fast yet effective speech emotion recognition with self-distillation
  We apply self-distillation to produce a fast and effective speech emotion recognition model, by simultaneously fine-tuning wav2vec 2.0 and training its shallower versions.

• PrototypeSound: Prototype Learning for Interpretable Respiratory Sound Analysis
  The prototype learning framework aims to generate prototypes of audio singnals for a respiratory sound classification task (normal/crackle/wheeze/both).

• HypercomplexECG: Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
  We propose lightweight convolutional neural networks for atrial fibrillation detection based on the recently proposed parameterised hypercomplex neural networks.

• Waveform Viewer: Graphical interface to explore ICU waveform data
  We implement a graphical interface in MATLAB for clincians to explore waveform data from bed-side ICU monitors, namely electrocardiogram, blood pressure and oxygen saturation signals.

• Knowledge Acquisition: The Effect of Masking Strategies on Knowledge Retention by Language Model
  In this work, we investigate how different training regimes affect the amount of factual knowledge that language models remember. We test for masking random words, entities, and masking multiple tokens based on point-wise mutual information.

• Knowledge Probing: BERTnesia: Investigating the capture and forgetting of knowledge in BERT
  We investigate how much factual knowledge is retained in the individual layers of language models. To do so, we use cloze questions (e.g., The capital of France is ___).

• Probing Search: Probing BERT for Ranking Abilities
  We investigate to what degree LMs for information retrieval encode standard abilities such as lexical or semantic similarity, named entity recognition, and others. We use that information where these models learn such abilities to train more effective models.

• Temporal Blind Spots: Temporal Blind Spots in Large Language Models
  We investigate to what degree LLMs are able to answer questions about historical events. Further, we explore common errors that occur when doing so.

• clinALL: AI-assisted clinical framework to facilitate diagnostic and translational discovery in hematological neoplasia
  clinALL is a clinical data integration, visualization and analysis framework especially designed for hematological neoplasia. Its main user interface is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the RNA sequencing data. Both clinical and genomic information provided by the users can be integrated and visualized on top of the UMAP.

• MPM: A Message Passing framework with Multi-data Integration for miRNA-Disease Association Prediction
  We propose a biologically-motivated data-driven approach for the micro RNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. Users can explore the predictions of our model through a WebApp. In addition, we make the biological information associated to miRNAs and diseases available.

• IVP-VAE: IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
  We propose a novel continuous-time model which can capture sequential patterns of EHR time series by purely solving multiple IVPs in parallel. By utilizing the invertibility property of IVP solvers, we achieve parameter sharing between encoder and decoder of the VAE architecture, and thus provide a more efficient generative modeling technique.

• Causality for Trustworthy AI: A Review of the Role of Causality in Developing Trustworthy AI Systems – Datasets and Packages
  This repository is a curated list of datasets used for recent Causal Machine Learning (ML) publications we covered in our survey. It also includes an overview of useful causal and non-causal tools and packages to assess different characteristics of ML models (e.g., robustness or fairness) and for use in healthcare.

• GeneMask: GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
  We propose a novel masking algorithm for Masked Language Modeling training of gene sequences. Here, we provide the resources required for (a) Computing for all 6-mers based on the Human Reference Genome, (b) PMI-best - pretraining and finetuning, (c) Datasets of Prom-core, Prom-300, and Cohn-enh used for the different few-shot settings, (d) de-novo motif discovery using the rGADEM R package.

• Knowledge Aware Med Classification: Knowledge-Aware Neural Networks for Medical Forum Question Classification
  We develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label, multi-class dataset where we annotate the existing CADEC dataset into five information need categories for the "Medical Forum Question Classification" task.

• MPSS Clinical Trial Search: Interpretable Clinical Trial Retrieval System using Pubmed Citation Network
  We propose a graph-based model that explores both clinical trials and the Pubmed databases to alleviate the shortage of relevant clinical trials for a query. We also contribute a disease-independent evaluation dataset for clinical trial search systems.

• Graph Learning Based AMDP: Graph learning-based generation of abstractions for reinforcement learning
  This work targets hierarchical reinforcement learning by incorporating a higher-level Markov decision process that helps speed up convergence of reinforcement learning.

• Deep Value Q-learning RL: Regulating Action Value Estimation in Deep Reinforcement Learning
  We propose a novel method called Deep Value Q-learning, which regulates the estimation of action values, tackles the overestimation issue of deep Q-learning and improves sample efficiency of reinforcement learning.

• Genie: Open Source Genome Compression Standard Codec
  The MPEG-G standard addresses the problem of efficient and cost-effective handling of genomic data by providing new compression and transport technologies and a family of standard specifications. We propose MPEG-G conformant software to compress genomic data, such as the first implementation of an MPEG-G compliant entropy codec, GABAC.

• GVC: Efficient Random Access Compression for Gene Sequence Variations
  We present the Genomic Variant Codec (GVC), a novel approach for compressing gene sequence variations with random access capability.

• PEKORA: High-Performance 3D Genome Reconstruction
  Reconstructing high-resolution genome structures efficiently and with high accuracy is challenging due to a high proportion of missing data and noisy observed interaction frequencies. To remedy this situation, we present PEKORA, a high-performance 3D genome reconstruction method using k-th order Spearman’s rank correlation approximation.

• ReITR: Relation Transformer for Scene Graph Generation
  We propose an end-to-end scene graph generation model RelTR with an encoder-decoder architecture. Our one-stage method can directly generate a sparse scene graph by decoding the visual appearance.

• TOAD-GAN: Coherent Style Level Generation from a Single Example
  We present TOAD-GAN (Token-based One- shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example.

• World-GAN: Generative Model for Minecraft Worlds
  We introduce World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample

• FoulingSeg: Semantic Segmentation of Macrofouling Images
  We present an approach for automatic image-based macrofouling analysis. We created a dataset with dense labels prepared from field panel images and propose a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes.