Publications

Norsk Regnesentral har etablert en statistisk risikomodell for vannskader etter overvannsflom på bygninger i Norge. Modellen kobler forsikringsdata fra Gjensidige sammen med nedbørdata fra seNorge og annen lokal eksponeringsinformasjon. Vi finner at risikoen for vannskader lar seg beskrive gjennom sesongvise mål på mengde kraftig nedbør og avvik fra typisk kraftig nedbør. Kombinert med klimaframskrivninger levert av Norsk Klimaservicesenter simulerer modellen forventede endringer i skadebeløp fra referanseperioden 1991–2020 til to framtidige scenarioperioder under et lavt, middels og høyt utslippsscenario for CO2. På nasjonalt nivå antyder simuleringene en økning på opptil 33 % fram mot slutten av århundret. Skadeframskrivningene er følsomme for variabiliteten i klimaframskrivningene, og vi anbefaler å utvise forsiktighet med bruk av lave og høye kvantiler av endringene i skadebeløp på kommune- og fylkesnivå.

Cognitive Digital Twins (CDTs) are virtual models of physical systems that integrate cognitive functions with real-time Internet of Things (IoT) data to support simulation, decision-making, and cyber resilience. In domains such as healthcare and smart cities, CDTs enable advanced capabilities but also introduce significant privacy and security risks, especially due to continuous, real-time data exchange and automated decision-making. This paper presents DC-TWIN, a dynamic consent-enabled CDT framework that addresses the limitations of static, one-time consent models in real-time, data-intensive environments. DC-TWIN introduces a multi-layered architecture consisting of: (i) a multi-layered architectural stack, including user control, policy management, dynamic trust, and data governance layers, that supports compliance monitoring, risk-aware user interfaces, consent history tracking, federated identity and role binding, and adaptive trust modeling, and (ii) a dynamic consent reasoning engine that uses contextual signals (e.g., user role, device status, network activity) and human factors (e.g., cognitive load, trust calibration, fatigue) to assess data access requests in real time, issuing granular decisions (grant, deny, prompt) or escalating for clarification. We highlight key use cases in healthcare, welfare technologies, and smart cities. The framework empowers users with real-time, contextual control over how their data is accessed, shared, and reused through adaptive interfaces and personalized consent mechanisms. By integrating dynamic consent reasoning, trust calibration, and continuous feedback loops, DC-TWIN supports transparent, compliant, and user-aligned data governance. It contributes to the secure and ethical deployment of CDTs by reinforcing user autonomy, enhancing risk communication, and enabling responsive consent management in critical domains such as healthcare.

The cyberattack surface in critical sectors is expanding due to the rapid proliferation of Internet of Things (IoT) devices. Artificial Intelligence (AI) models, such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), offer promising capabilities for detecting and classifying cyber threats. However, these models often struggle to generalize to previously unseen attacks after deployment. This study investigates how well different AI techniques can generalize to such novel threats in the presence of class imbalance. We evaluate three data balancing strategies: Generative Adversarial Networks (GAN), Synthetic Minority Over-sampling Technique (SMOTE), and class weighting. Experimental results indicate that DNNs outperform CNNs when provided with identical input data. While each balancing method has distinct advantages and trade-offs, the highest multiclass accuracy of 81.16 % was achieved by a DNN using GAN-augmented data for the previously seen attack types. The best performance on unseen attacks was achieved by a DNN trained with SMOTE, yielding a multiclass accuracy of 51 % among eight classes. The binary classification (benign vs. malicious) results were satisfactory, with DNN using GAN-augmented data achieving an accuracy of 99.20 %. These findings highlight the importance of not only separating data into training and test splits, but also incorporating a “seen vs. unseen” evaluation strategy.

Pre-trained encoders produce semantically rich latent embeddings, which, however, may expose unintended information through malicious inference or exploitation. We propose SEAL, a framework that mitigates embedding leakage by disrupting latent representations based on information-theoretic principles. It reduces the risk of potential misuse while enabling controlled reconstruction for trusted users. SEAL learns to encode controlled perturbations by minimizing the Matrix Norm-based Quadratic Mutual Information (MQMI) functional between original and perturbed embeddings within a hyperspherical latent space. Meanwhile, a private decoder, jointly trained with the SEAL encoder, is trained to reconstruct the original data that is accessible only to authorized users under an access-controlled setting. Extensive experiments on vision and text datasets demonstrate that SEAL reduces latent leakage, weakens the effectiveness of evaluated inference attacks, and preserves reconstruction under the considered setting.

In this work, we explore particle learning strategies for the joint estimation of static parameters and latent states within conditional sequential Monte Carlo (CSMC) algorithms. Building on this idea, we propose the p(parameter)-CSMC algorithm, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to Gibbs sampling in settings where strong internal correlations may challenge effective exploration. We include an application to the estimation of weights in a branching process model against synthetic data and show that, in this setting, performance is dramatically enhanced, with substantially faster mixing and markedly reduced autocorrelation compared with standard particle Gibbs implementations.

The Norwegian Centre for Research-based Innovation, Visual Intelligence, advances deep learning in marine science. The work focuses on analyzing multifrequency echosounder data to support ecosystem and fisheries management. To address limited labeled data for identifying sand eels in the North Sea, a semi-supervised learning method was developed that combines labeled and unlabeled data, significantly improving accuracy (Choi et al., ICES JMS 2021). Building on this, the method was extended to semantic segmentation (Choi et al., IEEE J. Ocean. Eng. 2023), enabling detailed classification of acoustic signals while reducing the need for costly annotations. More recently, foundation models were explored to tackle challenges like changing conditions in marine environments. By aligning these models with echosounder data and using semantic tokenization, they achieved strong performance with minimal labeled data (Choi et al., NAIS 2025). These innovations highlight the transformative role of AI in exploring and understanding the underwater world.

Arctic and Subarctic fisheries face increasing pressure as traditional practices struggle to keep pace with shifting ocean conditions. These challenges call for intelligent, adaptive systems to support sustainable monitoring and management. This session explores how Artificial Intelligence (AI) can contribute to the future of fisheries science by bridging algorithmic innovation with practical application and fostering interdisciplinary exchange. We invite papers that demonstrate how AI, ranging from machine learning and computer vision to emerging LLM-based agentic systems, can support fisheries research and regulation. Relevant applications include acoustic data interpretation for species identification, improved stock assessments, vessel activity monitoring, and adaptive regulatory strategies. Submissions addressing challenges such as limited computational resources, sparse data availability, or operational constraints in remote polar environments are particularly encouraged. The session also highlights the importance of engaging Indigenous knowledge holders and coastal communities in AI development. We especially welcome co-designed frameworks that promote fairness, transparency, and local agency in contexts where environmental data and governance intersect. Through this session, we aim to bring together diverse perspectives that advance responsible and locally grounded uses of AI for sustainable marine stewardship.

The uptake of practices labeled as "agile" is a topic of widespread discussion in research and practitioner communities. Within the broad topic of agility, there are discussions about the separation between agility in what one might call traditional software development, agility in the form of product orientation, and agility as expressed in continuous delivery. Although particular cases have been studied, the magnitude and manner of adoption and use of agile practices under these three themes remain unclear. We therefore sought to test hypotheses about their growth, prevalence, and implementation patterns in the Norwegian public sector. Aiming to form a comprehensive picture, we distributed surveys three years apart to IT executives at all Norwegian public institutions likely to build digital solutions. The results supported the view that agile practices are prevalent, but gave mixed support for their increase in use. We found no support for the view that agility in, respectively, software development, product orientation, and continuous delivery are treated as distinct disciplines in practice. We were also unable to identify other patterns in implementing these practices. The adoption of agile appears to be enabled primarily by commitment at the team and individual levels and inhibited by factors specific to the public sector. These findings should be compared with other sectors and countries. We propose issues for (a) further research on the scope of agile practices, (b) better indicators for adoption, (c) interaction among agile practices, and (d) factors that enable or inhibit the adoption of agile practices.

Utviklingen i innovasjonsprosjektet «landsdekkende myrdatasett» har vært preget av både teknologiske fremskritt og utfordringer knyttet til å etablere en landsdekkende KI-modell for kartlegging av myr og våtmark. Presnetasjonen tar for seg utviklingen og testingen av ulike modellutgaver, og hvordan de kan kombinere dem for å lage et mer robust og presist resultat, samt innsikt i testresultatene som foreligger.

We present a web application for controlling NAO V5 and NAO V6 robots using Hypertext Transfer Protocol Secure (HT TPS). The application was designed for a study in a special school. The school staff have found the application easy-to-use and versatile, and it may be useful to other researchers or people interested in controlling NAO. The HT TPS constraint and the locked-down nature of NAO introduced additional development challenges, and the solutions to these challenges are worth sharing with the HRI community. We present characteristics of the web application, implementation details, how it has been set up, and how to use it. Although the application is usable in its current form, there are still things that can be improved to make the application more useful in other contexts. We therefore document how the remote control can be extended and potential starting points for improvement.

Human-robot interaction (HRI) programmers often struggle with operating older robot hardware due to the short support period provided by manufacturers and difficulties integrating modern software solutions. This paper introduces the ZTL Task Library (ZTL), a lightweight communication framework and protocol designed to decouple robot hardware from the operating platform via socket communication, thereby increasing robot lifetime. We present a task-based communication protocol facilitating the co-design of robot behaviours with non-programming experts. Our approach has been shown across different platforms to effectively mitigate incompatibilities between middlewares, simplifying control and usability, allowing for simultaneous addressing of multiple devices.

Industrial Control Systems (ICSs), which are essential components of critical infrastructures, are inherently complex and vulnerable to cyberattacks. Advanced Persistent Threats (APTs) that target these systems are multi-stage, coordinated attacks that can lead not only to information loss but also to physical damage and loss of life. Traditional threat modeling approaches fall short in adapting to the dynamic nature of ICSs, necessitating new methodologies to predict and prevent such complex attacks. This work presents a digital twin-assisted dynamic threat modeling framework for ICS environments. The framework leverages a knowledge graph that integrates system data and cyber threat intelligence to predict potential attacks. In addition, the digital twin environment enables the validation of mitigation strategies before deployment in the physical system, while also supporting adaptive response and real-time mitigation. To predict the attacker’s next move, we propose a Relational Graph Convolutional Network (RGCN)-based model that utilizes enriched relational data such as tactics, campaigns, groups, techniques, and assets. The proposed RGCN model achieves a recall of 0.887, an F1-score of 0.893, and an AUC of 0.957 in predicting potential attack sequences. These results demonstrate that the model provides reliable and well-balanced predictive performance.

Å kompensere mennesker som bidrar med originalt innhold, handler ikke bare om rettferdighet. Det er også en investering i et bærekraftig, digitalt økosystem.

Abstract Objectives To investigate mammographic features associated with high artificial intelligence (AI) risk scores as provided by two AI models applied to screening mammograms. Materials and methods This retrospective study included 130,031 screening mammograms from 42,371 women attending BreastScreen Norway, 2008–2018. Two AI models (A and B) developed for cancer detection on screening mammograms were applied. An informed radiological review was conducted for mammograms within the highest 5% of AI risk scores by both models in two study samples: (1) High AI risk score, but no breast cancer detected within 6 years ( n  = 120), and (2) High AI risk score in mammograms with screen-detected cancers ( n  = 120). Mammographic density (BI-RADS a–d), features (mass, spiculated mass, asymmetry, architectural distortion, calcification alone, and density with calcification), and radiologists’ interpretation scores (1–5) were analyzed descriptively. Results Mammographic density was higher in sample 1 compared to sample 2 (BI-RADS d: 11% vs 3%, respectively). In sample 1, calcifications alone were the most frequent AI-marked feature (model A: 72%; model B: 68%), predominantly with amorphous morphology and a cluster distribution, and 76% were interpreted as benign by the radiologists (interpretation score 1). In sample 2, a spiculated mass was the most frequent mammographic feature among the screen-detected cancers (29%). Conclusion Mammograms assigned high AI risk scores exhibit distinct features depending on screening outcome. Systematic characterization of these features may help refine AI thresholds, improve specificity, reduce AI false-positive findings, and decrease the recall rate in breast cancer screening. Key Points Question Knowledge about mammographic features associated with high AI risk scores is essential for distinguishing cancer from non-cancer cases. Findings Calcifications were the dominant feature in non-cancers in screening mammograms with high AI risk score, whereas spiculated mass was the most frequent feature among cancers. Clinical relevance Calcifications in non-cancer screening mammograms with a high AI risk score were frequently interpreted as benign or probably benign by radiologists. This knowledge may help refine AI thresholds and thereby improve specificity and reduce false-positive results in mammographic screening. Graphical Abstract

Generativ kunstig intelligens er imponerende, men ikke alltid så nyttig til å løse industrielle problemer.

An increased focus on the blue economy across coastal African countries requires effective strategies for reducing marine resource conflicts to achieve goals of sustainable, equitable ocean development. We created a spatial database documenting marine resource conflicts (2008–2018) and conducted an expert survey to analyze patterns in conflict types and how they relate to actors, drivers, and resolution. Our findings indicate that 73% of conflicts were associated with access disputes and 28% were between non-fisheries sectors. National governments, small-scale or industrial fishers, and state enforcement agents were the most frequent actors. Illegal fishing, inequitable benefit distribution, and inadequate regulations were commonly reported conflict drivers. Less than one third of conflicts were resolved, but increased governance was cited as important for resolution. These results suggest policymakers may need to focus on access and benefit sharing issues and increase engagement of key actors in governance processes to realize blue economy ambitions.

Text sanitization is the task of redacting a document to mask all occurrences of (direct or indirect) personal identifiers, with the goal of concealing the identity of the individual(s) referred in it. In this paper, we consider a two-step approach to text sanitization and provide a detailed analysis of its empirical performance on two recently published datasets: the Text Anonymization Benchmark (Pilán et al., 2022) and a collection of Wikipedia biographies (Papadopoulou et al., 2022a). The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information. This privacy-oriented entity recognizer is trained by combining a standard named entity recognition model with a gazetteer populated by person-related terms extracted from Wikidata. The second step of the text sanitization process consists in assessing the privacy risk associated with each detected text span, either isolated or in combination with other text spans. We present five distinct indicators of the re-identification risk, respectively based on language model probabilities, text span classification, sequence labelling, perturbations, and web search. We provide a contrastive analysis of each privacy indicator and highlight their benefits and limitations, notably in relation to the available labeled data.

The Visual Intelligence Annual Report 2024 highlights the centre's progress, activities and achieved innovations for 2022. It describes new deep learning methods which address pressing societal needs in the fields of medicine and health, marine science, the energy sector, and earth observation.

The Visual Intelligence Annual Report 2021 highlights the centre's progress, activities and achieved innovations for 2021. It describes new deep learning methods which address pressing societal needs in the fields of medicine and health, marine science, the energy sector, and earth observation.

The Visual Intelligence Annual Report 2020 highlights the centre's progress, activities and achieved innovations for 2020. It describes new deep learning methods which address pressing societal needs in the fields of medicine and health, marine science, the energy sector, and earth observation.

Federated learning enables collaborative computation across multiple decentralized devices, minimizing data transfer overhead while enhancing privacy by keeping data local. However, it remains susceptible to inference attacks and potential data leakage. To strengthen privacy guarantees, especially for sensitive domains, advanced privacy-preserving techniques such as homomorphic encryption are recommended. This work proposes a privacy-preserving federated learning framework that integrates threshold homomorphic encryption into the federated learning pipeline to enable secure aggregation and protect intermediate computations. We employ threshold homomorphic encryption, a cryptographic technique well-suited for multiuser environments such as federated learning. We utilize the Cheon-Kim-Kim-Song (CKKS) scheme, as implemented in the OpenFHE library. Our approach extends the standard Federated Averaging (FedAvg) algorithm by homomorphically encrypting model updates and performing aggregation directly on encrypted data. To assess the trade-offs between efficiency and security, we evaluate the performance of the proposed method against a baseline. The design prioritizes practical constraints, including computational efficiency, making it suitable for deployment in privacy-sensitive domains such as healthcare and finance. To ensure compatibility with continuous integration and deployment (CI/CD) pipelines, all components of the solution are containerized using Docker.

When a database system is decommissioned, retaining its data, structure, and query capabilities is often crucial for future restoration and access. The Immortal Database Access (iDA) solution focuses on preserving decommissioned databases along with their stored information and retrieval functionalities. Building upon the Immortal Virtual Machine (iVM) technology, iDA provides tools that ensure long-term preservation of databases on physical storage media. This includes not only safeguarding the stored content but also enabling its regeneration with functional search capabilities. For this purpose, two innovative elements are introduced: DbSpec, a new language for managing the decommissioning process, and a Read-Only Access Engine (ROAE) which serves as an interface to future users who wish to retrieve the decommissioned information stored on the long-term substrate. ROAE complements the SIARD (Software Independent Archiving of Relational Databases) standard. Although SIARD is effective at preserving database data and metadata, it lacks the ability to capture the essential search and query functions necessary for meaningful information retrieval. iDA addresses this limitation, ensuring that decommissioned systems remain accessible and functional for future users.

Dette notatet oppsummerer arbeidet gjort i ReDUGUSON-prosjektet. Prosjektet ble gjennomført i samarbeid med VoiceOn AS og finansiert av FORREGION VIKEN. Prosjektet hadde til hensikt å kartlegge hvem som kunne dra nytte av innholdsopplesningstjenester. Vi holdt workshopper med personer med nedsatt syn og personer som hadde hatt slag og hadde afasi. I workshopene diskuterte man temaer knyttet til informasjon på offentlige sider, hjelpemidler og lovgivning. Deltakerne fikk mulighet til å undersøke tjenester som tilbød innholdsopplesning og ga tilbakemeldinger på dem. Noen ideer til forbedringer rundt etikk er presentert her. I tillegg skapte deltakerne sammen utkast til personaer og brukerreiser. De endelige utgavene av disse er presentert i rapporten. Deltakerne hadde også mulighet til å fylle ut et spørreskjema. Spørreskjemaet viste stor nytte av innholdsopplesning for deltakerne. Til slutt presenterer vi noen mulige steder for et hovedprosjekt.

Offentlige virksomheter er i ferd med å ta i bruk generativ kunstig intelligens (KI) for mange ulike oppgaver, blant annet tekstredigering og svar på spørsmål om tekster. For offentlig sektor er regelverk en særlig viktig type tekster, og to sentrale oppgaver knyttet til regelverk, som lover og forskrifter, er utforming og anvendelse av regelverket. Lover og forskrifter følger en bestemt logikk, der teksten er bygget opp av nøstede strukturer av forbud, tillatelser og unntak. For å arbeide med en slik tekst er det nødvendig å forstå hvordan denne strukturen gir den enkelte bestemmelse en semantisk kontekst. Vi studerer i hvilken grad og med hvilken kvalitet generativ KI kan håndtere oppgaver knyttet til forenkling og anvendelser av slikt regelverk. Våre undersøkelser viser at generativ KI per i dag er langt fra å være moden for slike oppgaver.

Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a large-scale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.

We propose a Long Short-Term Memory (LSTM) network to estimate historical daily streamflow and hydropower generation in Norway, with particular focus on run-of-river (ROR) plants. Historical records from such plants are often limited, and typically only contain hydropower generation data, which are truncated at the plants’ capacity limits and therefore do not capture high-flow conditions. The proposed LSTM model improves predictions in data-sparse and ungauged catchments, and for high-flow conditions, by learning from both hydropower generation data from ROR plants and streamflow data from other Norwegian catchments. Our model builds upon the neuralhydrology package, by adding a component that transforms streamflow into hydropower generation before loss calculations. The model is trained using streamflow and hydropower generation data from 190 Norwegian catchments and 136 ROR plants, with precipitation, temperature and catchment attributes as input variables. The LSTM model outperforms more traditional hydrological models for predictions in both gauged and ungauged catchments. Furthermore, the combined LSTM model yields hydropower generation estimates that are comparable to or better than those from a model trained only on hydropower generation data, while producing considerably better streamflow estimates. Our approach highlights the added value of additional data sources for hydrological modeling for both local calibration and the task of regionalization, and demonstrates that data-driven methods are suitable for leveraging their potential.

BRACE - et praktisk, datadrevet rammeverk for samhandling om deling av ressurser ved store og samtidige hendelser.

Dette er dokumentasjon av programvaren i LAVDAS slik den foreligger per mars 2025

In the AIforScreening project we have tested different ways of utilizing time sequence information in mammography screening, including published methods and home-grown ones. The simplest ones include regression modelling, where we apply a single-image breast cancer risk model at a sequence of images of a given breast and use linear regression to onstruct a modified score. This method givs a very modest improvement of the order 0.001 on the AUC scale, which was statistically significant only for the inferior holistic model. The more advanced methods try include co-registration of the current and previous images and various ways of merging the model’s features to produce improved risk scores, utilizing various so-called Siamese net models. Over-all, the results were negative, as none of the advanced methods gave improvements above the linear model. This is contrary to published results, and we speculate that this may be due to the fact that our model has a high performance to begin with, leaving less room for improvement. The linear model places positive weight on the previous risk scores, which go against the intuition that an increase in risk score over time should increase the likelihood of cancer. Apparently, the ’direct effect’ that an elevated risk score is associated with future cancer is stronger.

I denne rapporten beskrives arbeidet og resultater fra en utvidelse av validering og evaluering av produkter for snødekningsgrad (FSC) fra 2024. Hensikten har vært å undersøke hvordan avvikene (feil) i FSC-verdier, relativt til «fasiter» fra bilder med høyere oppløsning, varierer i tid og rom med aggregeringsnivå, arealdekke og terreng. Analysene er gjort for Østlandet og tilsigsområder valgt ut av NVE med satellittdata over flere år. Referansedata («fasiter») er basert på Sentinel-2 MSIprodukter i 10 m oppløsning, mens FSC-produktene som ble analysert, er basert på 0,5 km data fra Sentinel-3 SLSTR. Resultatene viser at avvikene har tydelig romlig og tidsmessig struktur. Romlig aggregering reduserer MAE og RMSE, mens bias i hovedsak bevares. En betydelig del av feilen midles likevel ikke effektivt ut ved aggregering opp til de største testede skalaene, noe som tyder på systematiske avvik over større områder. I høydesoneanalysene fremkommer et robust mønster med økte avvik rundt 400-500 m. Når FSC aggregeres til sone-middelverdier per dato reduseres avvikene sammenliknet med pikselbasert stratifisering, men mønsteret i høyde avtar ikke fullt ut. Avvikene er generelt større i skog enn i områder med bart fjell og sparsom vegetasjon, med en negativ bias i skog, som er konsistent med utfordringer knyttet til snø under trekroner. Samlet viser analysen hvordan avvik endrer karakter ved aggregering til modelleringsrelevante enheter, og peker på forhold som bør tas hensyn til ved bruk av FSC som areal- og høydesoneaggregert modellinput

Health systems in Norway and across Europe are under increasing strain from chronic conditions, long waiting times, and limited clinical capacity. At the same time, evidence-based digital therapeutics (DTx) are emerging as regulated tools to prevent, manage, and treat disease through clinically validated software interventions. Scandinavia has played a pioneering role in evaluating internet-delivered cognitive behavioural therapy for conditions such as insomnia and perinatal depression, demonstrating that well-designed, integrated digital solutions can complement existing healthcare services. However, despite promising trial evidence, large-scale adoption of DTx remains inconsistent due to regulatory, reimbursement, organisational, and cultural barriers. In parallel, simulation-based methods such as discrete-event modelling and digital twins are increasingly applied to optimise healthcare delivery and test alternative service configurations. Building on these developments, this project proposes the creation of an adaptive digital twin of a chronic care pathway to analyse how different deployment strategies for prescription digital therapeutics could impact system access, resilience, and resource utilisation.

In this study we have investigated how vision foundation models, pretrained on different domains, compete with a specialized model for classification as a function of the size of the labeled training set of medical images. Furthermore, we have looked into the different models' ability to generalize to difficult cases. Our experiments are conducted for cardiac ultrasound images and the downstream task of view recognition. Still, this classification task is meant to serve as a demonstrative example, where we think that the findings should be transferable to other classification tasks and other domains. Through these experiments we found that the foundation models were able to beat the performance of our task-specific supervised model when labelled training data were limited. This was true even for models trained on natural images and when using the simple linear probing method to create a classifier. We observed that more domain-specific foundation models achieved an even higher performance with limited data. On the other hand, the more general models showed a greater ability to generalize and perform well on difficult, out-of-distribution cases. Still, for typical in-domain cases with sufficient labeled data, a task-specific ResNet model was competitive with the foundation models, while also being both smaller and faster.

In this study we have investigated how vision foundation models, pretrained on different domains, compete with a specialized model for classification as a function of the size of the labeled training set of medical images. Furthermore, we have looked into the different models' ability to generalize to difficult cases. Our experiments are conducted for cardiac ultrasound images and the downstream task of view recognition. Still, this classification task is meant to serve as a demonstrative example, where we think that the findings should be transferable to other classification tasks and other domains. Through these experiments we found that the foundation models were able to beat the performance of our task-specific supervised model when labelled training data were limited. This was true even for models trained on natural images and when using the simple linear probing method to create a classifier. We observed that more domain-specific foundation models achieved an even higher performance with limited data. On the other hand, the more general models showed a greater ability to generalize and perform well on difficult, out-of-distribution cases. Still, for typical in-domain cases with sufficient labeled data, a task-specific ResNet model was competitive with the foundation models, while also being both smaller and faster.

In today’s rapidly evolving geopolitical climate, cybersecurity and digital sovereignty are more critical than ever for Europe’s resilience—and for investments in AI, healthcare and other critical sectors.

This user manual describes the COHIBA surface modeling software. It consists of: Part I Introduction: Basic ideas and terminology Part II User manual: Usage, input data, and results Part III Tutorials: Special topics such as volumes, simulation, and faults Part IV Reference manual: Descriptions of all COHIBA model file elements Part V Theory: Methods used by COHIBA Part VI Appendix: Release notes, known issues, references, list of acronyms, tables and figures, and an index

This user manual describes the COHIBA surface modeling software. It consists of: Part I Introduction: Basic ideas and terminology Part II User manual: Usage, input data, and results Part III Tutorials: Special topics such as volumes, simulation, and faults Part IV Reference manual: Descriptions of all COHIBA model file elements Part V Theory: Methods used by COHIBA Part VI Appendix: Release notes, known issues, references, list of acronyms, tables and figures, and an inde

Wide-beam or single-transmit acquisitions often reduce local spatial coherence, breaking the narrowband model assumed by high-resolution array spectral estimators such as IAA and Capon and thereby degrading performance. We propose a discrete prolate spheroidal sequence (DPSS) subspace projection of delay-focused aperture data. This projection suppresses incoherent off-angle energy and restores local spatial coherence, enabling scanline-wise adaptive spectral estimation under severe model mismatch. Each delay-focused aperture vector is projected onto a DPSS subspace spanned by the first K eigenvectors corresponding to a small angular bandwidth. The approach is lightweight, with precomputation and a per-point complexity of O(MK), and integrates naturally into standard delay-focused processing pipelines. Frequency–angle plots reveal how the projection reconstructs coherent ridge structures that are otherwise obscured by wide‑beam incoherence. Simulations in both plane-wave and diverging-wave ultrasound scenarios demonstrate improved resolution and contrast in single-transmit wide-beam imaging. Qualitative results on recorded channel data from the public PICMUS dataset provide an experimental sanity check and validation, indicating that the same coherence-restoration behavior is observed in real recordings. All experimental validation in this work is confined to ultrasound imaging; assessment of other array-processing applications is left for future work.

Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these do mains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://github.com/theabrusch/FreqRISE.

Vulnerability databases, such as the National Vulnerability Database (NVD), offer detailed descriptions of Common Vulnerabilities and Exposures (CVEs), but often lack information on their real-world impact, such as the tactics, techniques, and procedures (TTPs) that adversaries may use to exploit the vulnerability. However, manually linking CVEs to their corresponding TTPs is a challenging and time-consuming task, and the high volume of new vulnerabilities published annually makes automated support desirable.This paper introduces Triage, a two-pronged automated approach that uses Large Language Models (LLMs) to map CVEs to relevant techniques from the Att&ck knowledge base. We first prompt an LLM with instructions based on MITRE’s CVE Mapping Methodology to predict an initial list of techniques. This list is then combined with the results from a second LLM-based module that uses in-context learning to map a CVE to relevant techniques. This hybrid approach strategically combines rule-based reasoning with data-driven inference. Our evaluation reveals that in-context learning outperforms the individual mapping methods, and the hybrid approach improves recall of exploitation techniques. We also find that GPT-4o-mini performs better than Llama3.3-70B on this task. Overall, our results show that LLMs can be used to automatically predict the impact of cybersecurity vulnerabilities and Triage makes the process of mapping CVEs to Att&ck more efficient.

Two new different stochastic models for earthquake occurrence are discussed. Both models are focusing on the spatio-temporal interactions between earthquakes. The parameters of the models are estimated from a Bayesian updating of priors, using empirical data to derive posterior distributions. The first model is a marked point process model in which each earthquake is represented by its magnitude and coordinates in space and time. This model incorporates the occurrence of aftershocks as well as the build-up and subsequent release of strain. The second model is a hierarchical Bayesian space-time model in which the earthquakes are represented by potentials on a grid. The final ambition of the models is to make predictions on the occurrence of earthquakes.

The asymptotic behavior of several goodness-of-fit statistics for copula families is obtained under contiguous alternatives. Many comparisons between a Craméer-von Mises functional of the empirical copula process and new moment-based goodness-of-fit statistics are made by considering their associated asymptotic local power curves. It is shown that the choice of the estimator for the unknown parameter can have a significant influence on the power of the Craméer-von Mises test, and that some of the moment-based statistics can provide simple and efficient goodness-of-fit methods. The paper ends with an extensive simulation study that aims to extend the conclusions to small and moderate sample sizes.

In this paper we develop statistical models for bankruptcy prediction of Norwegian firms in the limited liability sector using annual balance sheet information. We fit generalized linear-, generalized linear mixed- and generalized additive models in a discrete hazard setting. It is demonstrated that careful examination of the functional relationship between the explanatory variables and the probability of bankruptcy enhances the models' forecasting performance. Using information on the industry sector we model the unobserved heterogeneity between different sectors through an industry-specific random factor in the generalized linear mixed model. The models developed in this paper are shown to outperform the model with Altman's variables at all levels of risk. As a measure of models' forecasting accuracy the area under the ROC curve is used.