Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists with their tasks and ease the workload. In this paper, we are proposing a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas and an augmentation method that can improve classification results. The proposed model is a fused combination of truncated pre-trained DenseNet201 and ResNet101V2 as a patch-wise classifier followed by a lightweight U-Net as a refinement model. We have used two datasets (Norwegian Lung Cancer Biobank and Haukeland University Hospital lung cancer cohort) to create our proposed model. The DRU-Net model achieves an average of 0.91 Dice similarity coefficient. The proposed spatial augmentation method (multi-lens distortion) improved the network performance by 3%. Our findings show that choosing image patches that specifically include regions of interest leads to better results for the patch-wise classifier compared to other sampling methods. The qualitative analysis showed that the DRU-Net model is generally successful in detecting the tumor. On the test set, some of the cases showed areas of false positive and false negative segmentation in the periphery, particularly in tumors with inflammatory and reactive changes.

Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and air pollution data from three major cities in Norway. Findings from feature selection reveal a correlation between rising heating degree days and heightened air pollution levels, suggesting increased heating activities in Norway are a contributing factor to worsening air quality. PBDL demonstrates superior accuracy in air pollution predictions compared to LSTM. This paper contributes to the growing literature on PBDL methods for more accurate air pollution predictions using environmental variables, aiding policymakers in formulating effective data-driven climate policies.

While the purpose of the study was not to prove that AI systems are capable of replacing humans in creative roles, it raises philosophical questions about the characteristics that are unique to humans, says Simone Grassini, an associate professor of psychology at the University of Bergen, Norway, who co-led the research. "We've shown that in the past few years, technology has taken a very big leap forward when we talk about imitating human behavior," he says. "These models are continuously evolving." Proving that machines can perform well in tasks designed for measuring creativity in humans doesn't demonstrate that they're capable of anything approaching original thought, says Ryan Burnell, a senior research associate at the Alan Turing Institute, who was not involved with the research. The chatbots that were tested are "black boxes," meaning that we don't know exactly what data they were trained on, or how they generate their responses, he says.

Marine debris originating from human activity has been accumulating in underwater environments such as oceans, lakes, and rivers for decades. The extent, type, and amount of waste is hard to assess as the exact mechanisms for spread are not understood, yielding unknown consequences for the marine environment and human health. Methods for detecting and mapping marine debris is therefore vital in order to gain insight into pollution dynamics, which in turn can be used to effectively plan and execute physical removal. Using an autonomous underwater vehicle (AUV), equipped with an underwater hyperspectral imager (UHI) and stereo-camera, marine debris was autonomously detected, mapped and quantified in the sheltered bay Store Lungegaardsvann in Bergen, Norway.

Manic episodes of bipolar disorder can lead to uncritical behaviour and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode and symptoms such as euphoria and increased productivity can also deter affected individuals from seeking help. This work proposes to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This paper proposes a new deep learning-based ensemble method leveraging long (20h) and short (5 minutes) time-intervals to discriminate between the mood-states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.

Julien Brajard 1,2, Alberto Carrassi 3,4, Marc Bocquet 5 and Laurent Bertino 1 1 Nansen Center (NERSC), 5006, Bergen, Norway 2 Sorbonne University, Paris, France 3 Department of Meteorology, University of Reading and NCEO, United-Kingdom 4 Mathematical Institute, University of Utrecht, The Netherlands 5 CEREA, joint laboratory École des Ponts ParisT ech and EDF R&D, Université Paris-Est, France In recent years, machine learning (ML) has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train MLbased parametrisation using direct data, in the realistic scenario of noisy and sparse observations. The algorithm proposed in this work is a two-step process. First, data assimilation (DA) techniques are applied to estimate the full state of the system from a truncated model. The unresolved part of the truncated model is viewed as a model error in the DA system. In a second step, ML is used to emulate the unresolved part, a predictor of model error given the state of the system. Finally, the MLbased parametrisation model is added to the physical core truncated model to produce a hybrid model. The DA component of the proposed method relies on an ensemble Kalman filter while the ML parametrisation is represented by a neural network. The approach is applied to the two-scale Lorenz model and to MAOOAM, a reduced-order coupled ocean-atmosphere model.

By: William Schoenberg (University of Bergen, Norway) Abstract This work re presents a new approach which generates then analyzes a highly non - linear complex system of differential equations to do interpretable time series forecasting at a high level of accuracy. This approach provides insight and understanding into the mechanisms responsible for gener ating past and future behavior. Core to this method is the construction of a highly non - linear complex system of differential equations that is then analyzed to determine the origins of behavior. This paper demonstrates the technique on Mass and Senge's two state Inventory Workforce model ( 1975) and then explores its application to the real world problem of organogenesis in mice . The organogenesis application consists of a fourteen - state system where the generated set of equations reproduces observed behavior with a high level of accuracy ( 0.88 0 Introduction: Accurate time series forecasting is very important to a variety of scientific fields, engineering disciplines, and socially constructed systems including businesses, and governments (Palit & Popovic, 2006) . Past effort s on this problem have focused on developing more accurate methods or models useful for predicting time series data, starting with linear statistical models and evolving into non - linear models and ultimately machine learning techniques (Bontempi, et, al, 2012) .

Amazon CTO and VP, Werner Vogels travels to the scenic fjords in Bergen, Norway to meet with the founder of Aquabyte to learn how AI is playing a new role in salmon farming.

Smart assistants could come with a'moral AI' to decide whether to report their owners for breaking the law. That's the suggestion by academics at the University of Bergen, Norway, who touted the idea at the ACM conference on Artificial Intelligence, Ethics and Society in Hawaii. They suggest that domestic bots such as Amazon Echo and Google Home should be enhanced with moral AI. This would enable them to weigh-up whether to report illegal activity to the police, effectively putting millions of people under constant surveillance. Marija Slavkovik, Associate Professor the Department of Information Science and Media Studies, led the research behind the idea.

Training neural network for object recognition (a car with front lights) in action. Improving traffic safety, better health services and environmental benefits – Big Data experts see a wide range of possibilities for advanced image analysis and recognition technology. "Advanced image recognition by computers is the result of a great deal of very demanding work. You have to mimic the way the human brain distinguishes significant from unimportant information," says Eirik Thorsnes at Uni Research in Bergen, Norway. Thorsnes heads a group in the company's Centre for Big Data Analysis focus area, which develops strategies for use of big data for research and commercial purposes.