Backpropagation, the standard for training Convolutional Neural Networks (CNNs), is not biologically plausible due to its reliance on forward and backward passes. In December 2022, Professor Geoffrey Hinton, a pioneer in the field, introduced the Forward-Forward (FF) algorithm as a potential alternative. FF avoids backpropagation throughout the entire network by using two forward passes,...
Diagnosing epilepsy after a first unprovoked seizure, especially without visible lesions and with a normal rEEG, is challenging. Understanding EEG network changes requires data collected close to the acute event and follow-up information, necessitating a large data source. The UMG database, with over 34,000 routine EEGs, is invaluable for this purpose. In our study, it took a month to select...
As one of the first of its type in Germany, KISSKI has received a test and development board for the SpiNNaker-2 neuromorphic platform, in preparation for a larger system installation in late 2024. This test board will be made available to interested researchers shortly, and the full platform will be offered as a regular KISSKI service to both academic institutions and industry partners....
Every day, hospital professionals face the challenge of navigating through extensive patient information that is also often not available at the point of care. The complexity and volume of data, along with time pressure, can easily lead to critical information being overlooked. With the digitization of patient records, an adaptive information provision system such as CAIS.ME (Context-Aware...
We introduce DOSMo-7B, an open 7 billion parameter large language model (LLM) trained on 1T tokens of exclusively German text. DOSMo-7B uses the same architecture as Mistral-7B, paired with a custom tokenizer to maximize the encoding efficiency for German text. In contrast to existing approaches, which typically improve the German skills of LLMs with continued pretaining, we perform from...
Artificial Intelligence (AI) has seen a significant surge in popularity, particularly in its application to medicine. The exponential increase in examinations by 3D imaging devices such as CT and MRI has resulted in a massive volume of image data, necessitating the use of AI. Typically, doctors interpret these scans in a time-consuming process often limited by subjectivity, image complexity,...
As part of the project Area-wide delineation of small-scale suitability areas for heat planning (FLAKE), suitability areas for heat supply with heating networks are to be identified. The pro-ject is being carried out in cooperation with GSG Oldenburg. Various geo-AI algorithms will be used to find the best possible area allocations. At the beginning of the project, the data from the housing...
Scholarly knowledge curation faces challenges due to diverse methodologies across scientific fields. Leveraging Large Language Models (LLMs) like GPT-3.5 and visual models, we enhance AI explainability and trustworthiness in knowledge curation. Our approach integrates LLMs and VLMS with the Open Research Knowledge Graph (ORKG) and employs prompt engineering for accurate data extraction from...
Biological neurons exhibit a large degree of heterogeneity. In a recent work, we have shown that heterogeneity in the timescales of rate-based neurons can be exploited for a better input representation in networks, leading to better performance on various tasks comprising nonlinear transformations of time-shifted input. More specifically, we have used a recurrently balanced network driven by...
Accurate weather forecasts are core ingredients for many applications.
Farmers rely on weather forecasts to plan planting, irrigation, and harvesting. Airlines use weather data to plan flight routes, ensure passenger safety, and mini-mize delays. Utility companies use forecasts to anticipate demand changes due to temperature variations and to manage resources efficiently.
Current state...
We present a framework using automatic differentiation with JAX to estimate the parameters of a dynamical system within a Bayesian framework and showcase two examples. First, we estimate the time-dependent reproduction number underlying COVID-19 cases in the UK. Second, we infer the most probable times of decision-making in a cognitive experiment. Our framework thereby provides a systematic...
Mechanical ventilation (MV) is a life-saving therapy used in the intensive care unit (ICU). However, improper settings can lead to lung injury and organ damage. Determining the ideal ventilation settings is challenging due to the large number of variables involved, making it difficult to provide clear guidelines.
The IntelliLung project addresses this issue with a reinforcement learning (RL)...
Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing...
Finding the optimal workflow to clean and prepare data, train a machine learning (ML) model, select between different models, and evaluate the final model properly is a complex task. Novice users can easily be overwhelmed by the variety of available methods for the individual workflow steps like data splitting, metric selection, and model evaluation. Many tools have been created to support...
Neuromorphic computing systems mimic the structural architecture and computational strategies of the human brain, with the aim to boost the performance of artificial intelligence applications as well as simulations of biological brain dynamics. To reach this goal, several technologies exist, following different approaches.
Loihi 2, developed by Intel, leverages hardware implementation of...
As the expansion of renewable energies grows, the need for accurate energy forecasts becomes crucial due to the dependency on volatile energy sources. Traditional forecasting systems, which utilize weather data and historical generation data, are challenged by the unique behaviors of individual power plants, lack of data and changing conditions (Yan et al. 2022). To address these challenges,...
According to recent studies in the field of Public Health, over half of the German population indicates having difficulties finding, comprehending, appraising and using health-related information (Schaeffer et al. 2021). The German National Action Plan Health Literacy lists Plain Language as a communicative tool to make health information more accessible to large population groups (Schaeffer...
With the increasing integration of renewable energy and evolving power consumption patterns caused by new consumers like electric vehicles and heat pumps, power flows in the electricity grid have become more fluctuating and weather-dependent, challenging grid stability. Accurate power forecasts are essential for grid operators to ensure reliable grid calculations and planning. We present a...
Semares is a data integration platform solution developed by Genevention GmbH. Semares facilitates integration, analysis and exploration of life science data and provides programmatic as well as push-button access for bioinformatics and AI applications.
Improving the spatial resolution of satellite images offers considerable potential for a wide range of remote sensing applications. This study investigates the use of an Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) to improve the resolution of Sentinel-2 satellite imagery, using high-resolution digital orthophotos as ground truth for fine-tuning. The Real-ESRGAN...
Large language models (LLMs) have shown promising capabilities in several domains, but “Inherent Bias,” “Data Privacy & Confidentiality,” “Hallucinations,” “Stochastic Parrot,” and “Inadequate Evaluations” limit the LLM’s reliability for direct and unsupervised use. These challenges are exacerbated in complex, sensitive, low-resource domains with scarce large-scale, high-quality datasets....
With the help of experts from Forschungszentrum Jülich, the u-form publishing house aims to develop an AI tutor for exam preparation. As part of a joint research project with the AI service centre WestAI, various LLMs are being tested and integrated into the publisher’s training programmes.
The research collaboration is about evaluating free text answers to exam questions with the help of...
