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Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed. Aim: This study aims to complement existin
We created an AI support for diagnosis in dyspneic adults at time of triage in the emergency department.Complete data from an entire regional health care system was analyzed, to find AI-derived, unknown, important diagnostic predictors. Most important were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life difficulties and maternal care.Sensitivity for AHF,
Previous work has shown that exposure to auditory white noise (WN) can improve cognitive performance in children with ADHD, but it is unknown whether this improvement generalizes to other sensory modalities. To address this knowledge gap, we tested the effect of Stochastic Vestibular Stimulation (SVS) on cognitive performance and reaction time (RT) variability in two groups: children with ADHD and
We built machine learning and image analysis tools in order to forecast winter wheat yield based on a rich multi dimensional tensor of agricultural information spanning different scales. This information consists of satellite multi-band images, local soil samples obtained from national databases, local weather as well as field data from 23 farms cultivating winter wheat in southern Sweden. This is
Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL•E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, prohibited by the lack of text-lidar datasets. In this work, we propose LidarCLIP, a mapping from automotive point clouds to a pre-existing CLIP embeddin
Simultaneous sound event localization and detection (SELD) for multi-source sound events is an open research field. The Multi-ACCDOA format is a popular way to handle activity-coupled sound events where the same class occurs at multiple locations at the same time. An important part is the Auxiliary Duplicating Permutation Invariant Training (ADPIT) paradigm that calculates the loss for order-agnos
Songbird migration involves frequent migratory flights interrupted by several days of stopover to refuel. For first-year migratory birds, this entails stopping in unfamiliar locations to exploit local resources and maximise fuelling rates. However, stopovers also pose mortality risks due to predator presence. We aimed to determine whether auditory cues from avian predators with differing hunting s
Today, the advances in airborne LIDAR technology provide high-resolution datasets that allow specialists to detect archaeological features hidden under wooded areas more efficiently. Still, the complexity and large scale of these datasets require automated analysis. In this respect, artificial intelligence (AI)-based analysis has recently created an alternative approach for interpreting remote sen
In this paper we revisit the Time-of-Arrival self-calibration problem. In particular we focus on imbalanced problem instances where there are significantly more sources compared to the number of receivers, which is a common configuration in real applications. Using an implicit representation, we are able to re-parameterize the sensor node self-calibration problem using only the parameters of the r
Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality anno
Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major sca
We propose a novel hybrid approach that integrates Neural Ordinary Differential Equations (NODEs) with Bayesian optimization to address the dynamics and parameter estimation of a modified time-delay-type Susceptible-Infected-Removed (SIR) model incorporating immune memory. This approach leverages a neural network to produce continuous multi-wave infection profiles by learning from both data and th
Ex vivo working porcine heart models allow for the study of a heart’s function and physiology outside the living organism. These models are particularly useful due to the anatomical and physiological similarities between porcine and human hearts, providing an experimental platform to investigate cardiac disease or assess donor heart viability for transplantation. This chapter presents an in-depth
The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. Th
Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for
Triple-negative breast cancer (TNBC) accounts for 10-20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumour immune response can be prognostic in treatment-naive and chemotherapy-treated TNBC patients and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a
This paper presents a comprehensive evaluation of various wireless networks for Teleoperated Driving (ToD) applications, focusing on a hoverboard as a test vehicle. The study compares end-To-end delay and jitter across five network setups: A private 5 G network, a wireless LAN, a public LTEA network, and two wireless CANs, based on the service level requirements outlined by the 5G Automotive Assoc
