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Algebraic Theory of Information Quantities

Information theory is a discipline at the intersection of statistics, engineering and computer science. As the study of information quantities, such as compression or communication capacities, information content or measures of statistical dependency, it is one of the theoretical underpinnings of data science.

Computational problems in information theory are highly non-linear. The goal of this project is to transfer state-of-the-art methods of computational non-linear algebra to information theory, to study the inherent algebraic complexity of information-theoretical problems and to provide tools for solving them in practice. The algebraic point of view has proven to be fruitful in seemingly unrelated areas, as witnessed by a surge of recent work in algebraic statistics, in particular on likelihood geometry. However, maximizing the likelihood function is the same as minimizing relative entropy — a specific information quantity. Hence, this project also aims at generalizing the techniques developed in likelihood geometry.

One focus is on the practical computation of information quantities using numerical and differential algebraic geometry. Such quantities are defined via non-linear optimization problems and we aim to pinpoint the algebraic complexity of these problems in instances of general interest, such as common information measures. The final objective is finding fundamental laws and limits of data science imposed by non-linear inequalities constraining the entropic region. These inequalities provide, by duality, universal bounds for many of the optimization problems studied in information theory.

WHeelchair Activity Monitoring project (WHAM)

Wheelchair users (WCU) are three to five times more inactive than the general population, leading to a high risk of developing preventable secondary conditions. Although physical activity (PA) guidelines have been specifically developed for WCU, we lack systematic approaches to implement them in practice, as well as valid algorithms to monitor PA change in WCU through sensor-based solutions. Accordingly, the overall scientific aim of the WHeelchair Activity Monitoring project (WHAM) is to advance PA research and practice in WCU by developing valid digital markers for monitoring of and providing feedback on PA, paving the way for implementation and intervention. I will accomplish this through combining the complementary research fields of 1) behavioural intervention studies focusing on increasing PA in WCU (core competence University of British Columbia, UBC; outgoing phase) and 2) sensor-based PA monitoring (core competence Norwegian University of Science and Technology, NTNU; returning phase – consolidated by non-academic placement at Maastricht Instruments, which develop research prototypes including PA monitors). WHAM’s scientific aim is achieved through three objectives: 1) Measure the effect of a pragmatic exercise intervention trial on self-reported physical activity levels and cardio-respiratory fitness, 2) Develop valid algorithms for tracking PA markers in WCU, and 3) Co-create a framework that outlines how PA can be conjointly monitored and provided feedback on in WCU. This fellowship will enable me to become established as an internationally recognized researcher in the field of “physical activity and disability”, while bringing together ongoing national disjointed research activities in this field. Altogether, my overall ambition is to contribute to reducing inequalities in daily-life participation among WCU compared to the rest of the population.

EOSC Beyond: advancing innovation and collaboration for research

EOSC Beyond overall objective is to advance Open Science and innovation in research in the context of the European Open Science Cloud (EOSC) by providing new EOSC Core capabilities allowing scientific applications to find, compose and access multiple Open Science resources and offer them as integrated capabilities to researchers. To do so, EOSC Beyond supports a new concept of EOSC: a federated and integrated network of Nodes operated at different levels, national, regional, international and thematic, to serve the specific scientific missions of their stakeholders. Further specific objectives of the project are to accelerate ‘time to product’ of new scientific applications with software adapters, enable Open Science with machine composability and dynamic deployment of shared resources, support innovation in EOSC with a testing and integration environment, and align the EOSC Core architecture and specifications to integrate with European dataspaces. The project extends the state of the art of the EOSC Core and adopts a co-design methodology, including requirements elicitation, software development and validation in collaboration with different use cases from EOSC national and regional initiatives (e-Infra CZ, Czechia, NFDI, Germany, and NI4OS, South East Europe region), thematic research infrastructures from Social Sciences and Humanities (CESSDA), Life Sciences (CNB-CSIC and Instruct-ERIC), Environmental Science (ENES and LifeWatch), and Health and Food (METROFood-RI). EOSC Beyond builds on the capacities of prospective EOSC Nodes and partners with multi-annual experience in developing solutions for large-scale federated digital infrastructures and aligns with the technical architecture and requirements of data spaces from different business sectors. Ultimately, EOSC Beyond supports Open Science in modern, data-intensive, and multidisciplinary research, facilitating resource discovery, access, and reuse across scientific communities, organisations, and countries.


Harmonization of Advanced Materials Ecosystems serving strategic Innovation Markets to pave the way to a Digital Materials & Product Passport

The overarching objective of DigiPass is to enhance the digital maturity of the European communities that develop materials and intermediate products. The project will develop recommendations and clear routes toward digitalized circular business models. The overarching key result of DigiPass is to create a sustainable platform which includes support for Digital Materials & Product Passport and for collaborative innovation-by-design processes in a circular economy served by advanced materials. A business model for operating such a platform completes the overarching objective.
DigiPass is harmonizing and synergizing collected materials data sources and digital infrastructures. This will enable interoperability of data exchange, and standardization of advanced materials knowledge representation at all maturity levels. Accelerating the design, development, and production of advanced, safe, and sustainable chemicals and materials, as they are necessary for innovative products, calls for a collaborative approach involving different stakeholders to support durability, repair and overhaul, reuse, and recyclability of products. DigiPass project impacts all materials development communities over the whole circular value chain. 


Enhanced X(cross)-disciplinary Community-driven Imaging Technologies for Earth and Environmental material research

The EXCITE² Network is revolutionizing Earth and environmental material science research by consolidating a critical mass of 18 research facilities in 12 European and associated partner countries into a unified infrastructure and providing transnational access to advanced imaging technologies. This enables scientists to decipher complex processes within Earth materials at scales from nanometres to hundreds of centimetres, gaining unparalleled insight into the chemical and physical processes that govern, among others, environmental toxicity and its impact on human health, critical metal extraction for renewable energies, and the utilisation of Earth materials as long-term storage for climate-altering gases. The consortium fosters knowledge-sharing and talent attraction, bolstering the problem-solving capacity of the European Research Area. Moreover, EXCITE² is driving interdisciplinary collaboration and cross-fertilisation across academic institutions, scientific disciplines, and industry, propelling Europe towards a sustainable future. The initiative offers dedicated training programmes to a new generation of researchers within the European open science landscape, paired with access to world-class imaging facilities and expertise for problem-solving research and innovation. EXCITE² is introducing innovative service developments, such as artificial intelligence and leading-edge imaging experiments, to increase the user's problem-solving capacity. In addition, the consortium is implementing customised project valorisation strategies to maximize the impact of research outcomes in academia, society, and the economy. Citizen science projects allow the public to participate directly within the EXCITE² Network, contributing to solving EU challenges. As such, EXCITE² is paving the way towards a sustainable future, driving scientific excellence, and creating positive societal impact.

Engineering carbon quantum dots for zinc ion hybrid capacitors

Zinc ion hybrid capacitors (ZIHCs) integrate two energy storage mechanisms of battery-type electrode and capacitive electrode, bearing the advantages in energy density, power density, and safety. Until now, the cycle retention rate of ZIHC's capacity reported is still not more than 80%, restricting its development and commercial application severely. Focusing on the issue, the project aims at overcoming the limitation of traditional electrode materials and re-defining the designing concept to construct Carbon Quantum Dots (C-QDs) for ZIHCs. Advanced methods will be used to harvest carbon quantum dots from coal tar pitch (a sort of industrial solid castoff). The bulk sp2-carbon network, innumerable edge sites, profuse surface functional groups, and regular size ensures that as-synthesis C-QDs have high electrical conductivity, sufficient ion transport channels and rich adsorption sites. Moreover, the new concept of dynamic evolution is introduced for insight into the structure and interface failure of ZIHC's electrode. The interface-reconstruction mechanism and the attenuation model of capacity will be established for the first time. Further, the methods to strengthen structures of C-QD electrode will be developed based on a trade-off strategy. The ultimate goal is to improve the energy storage efficiency of ZIHCs by 20% and to increase their cycle performance more than 95% unprecedentedly. This research will develop long-running high performance ZIHCs, for contributing European energy storage industry. The ER will achieve abundant research experience and scientific skills from the project and the capability to launch her own research group in future.

IMProving User experience, Long-term sustainability and Services of EU-OPENSCREEN

EU-OPENSCREEN (EU-OS), the distributed European Research Infrastructure for Chemical Biology and early drug discovery, provides world-class services in high-throughput compound screening and medicinal chemistry to users from academia and industry. As one of the largest open-access initiative globally, EU-OPENSCREEN plays an increasingly important role in facilitating early stage drug discovery in Europe. It recently integrated fragment-based drug discovery and chemoproteomics as two new services, but the increasing complexity of modern drug discovery projects requires an even more comprehensive portfolio and integration of emerging technologies. To ensure that EU-OS continues to offer a comprehensive suite of relevant services and support a growing user community to conduct forefront research, IMPULSE will develop, validate and integrate new services in AI/ML, new chemical modalities, and innovative disease-relevant cell models combined with CRISPR-Cas9/RNAi-based genetic screening. IMPULSE will work towards the implementation of common operational and data standards to increase data reproducibility and FAIRness. Bespoke outreach and training activities will raise awareness among key user groups of its new services and advance operational excellence and reproducibility in pre-clinical drug discovery. New models for pre-competitive drug discovery with industry or charities, and growing ERIC member community will reinforce the sustainability and accessibility of EU-OPENSCREEN.

Aqua Research Infrastructure Services for the health and protection of our unique, oceans, seas and freshwater ecosystems

AQUARIUS will provide a highly comprehensive suite of integrated research infrastructures appropriate to addressing significant challenges for the long-term sustainability of our unique oceans, seas and freshwater ecosystems. For the first time, diverse research infrastructures will be combined to facilitate the work of researchers and key stakeholders focused on challenges and opportunities for both marine and freshwater systems. An impressive range of 57 research infrastructure services will be made available to include research vessels, mobile marine observation platforms, aircraft,  drones, satellite, sensors, fixed freshwater and marine observatories and test sites, experimental facilities, and sophisticated data infrastructures. AQUARIUS will support the development phase of the EU Mission to Restore our Ocean and waters by 2030, the Sustainable Blue Economy Partnership, the European Green Deal, and international climate initiatives. It will also be an essential component in achieving the European Digital Twin of the Ocean and the UN Decade for Ocean Sciences. The needs of researchers will be met through a robust and transparent system of transnational access funding Calls, facilitated by centralised user-friendly access portal. The Call programme will be informed through stakeholder engagement and brokerage events. Projects to be selected for Access must convincingly integrate multiple infrastructures and contribute to the core policy objectives of Mission Ocean, that is, to protect and restore marine and freshwater ecosystems and biodiversity; to prevent and eliminate pollution of our oceans, seas and waters; and to ensure a sustainable, carbon-neutral and circular blue economy. A thematic and geographic focus will be the hallmark of the proposed transnational Calls, aligning with the Lighthouse Regions, that is, the Baltic and the North Sea Basins, Black Sea, Atlantic/Arctic, and Mediterranean Sea along with their associated rivers.

EOSC-ENTRUST: A European Network of TRUSTed research environments

The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 
Countries and institutions have made significant investments in secure and trusted data environments to meet the need for research and policy-driven analysis of sensitive datasets such as people's health and socio-economic status, the habitats of vulnerable species and geo-spatial locations of protected sites. There are now a large number of such secure environments in operation - linked to national centres, individual organisations or specific research communities. This fragmented landscape poses challenges for both users and providers: researchers are faced with a large number of different systems and access procedures; providers need to manage federated access across multiple, potentially incompatible, technology and governance frameworks.
EOSC-ENTRUST brings together providers of operational Trusted Research Environments from 15 European countries with a shared goal to implement, validate and promote their capabilities through a common European framework using shared standards and common legal, operational and technical language. This blueprint for interoperability is anchored in the EOSC Interoperability Framework spanning the four dimensions of Legal, Organisational, Technical and Semantic interoperability. EOSC-ENTRUST has identified four driver projects covering Genomics, Clinical trials, Social science, and public-private partnerships to benchmark capabilities, inform blueprint design and demonstrate secure  data analysis using federated workflows.
Targeted outreach activities will expand this open network with further providers and develop policy papers and guidelines for the full range of stakeholders to create a long-term operational TRE framework within the European Open Science Cloud.


Providing research infrastructure services to support Next Generation EU

This four-year project will harness outputs from key social science RIs in Europe and other reliable sources to support the aims of the ‘Next Generation EU’ programme and to inform EU youth policy. Virtual access will be provided to the outputs through special installations (portals) developed during the project. The core of the proposal is a set of existing research ‘services’, which will be re-purposed and customised to focus directly on the five themes: Make it Green; Make it Digital; Make it Healthy; Make it Strong; and Make it Equal. 

For each area, the consortium will work collaboratively to produce seven sets of resources:

i)	A searchable inventory of relevant variables drawn from multiple cross-national datasets; 

ii)	Analytical summaries, suitable for policy makers and analysts new to the field, as well as interactive resources available through the project portal;

iii)	Harmonised and merged extracts from these datasets that reduce the burden on analysts and increase sample sizes;

iv)	New data collected through the ESS’s established CRONOS web panel infrastructure. Eleven countries will field five waves of the panel, each one dedicated to a NextGenEU theme. Free and rapid access to the data will be available through the CRONOS portal alongside analytical summaries;

v)	In-person and virtual deliberative forums with young people (aged 18-34) in four countries covering the five Next Generation EU’ themes;

vi)	An educational tool (E-NextGen) channelling our data for use in classrooms and by the general public;

vii)	Comprehensive training materials related to all project outputs.


Midlene fra SSF-Svalbard Science Forum fordelt på år

Midlene fra SSF-Svalbard Science Forum fordelt på fylker

Midlene fra SSF-Svalbard Science Forum fordelt på fagområde

Masters student

The term "hunger gap" (no: “vårknipa”) traditionally used in agricultural communities, refers to the period at the end of winter when winter provisions are depleting, yet the growing season has not yet started. In contemporary usage, the term also describes the food situation for wild animals, which may encounter challenging conditions at the end of winter. This situation is particularly pronounced in the Arctic, where the Svalbard reindeer is awaiting new plant growth after an eight-month-long winter. The limited winter food supply has prompted various morphological, physiological, and behavioral adaptations to conserve energy, crucial for overcoming the "hunger gap". The aim of my thesis is to investigate annual and individual variation in what ends the “hunger gap” for reindeer. I will utilize a dataset of activity and heart rate monitoring of ca 50 GPS-marked female Svalbard reindeer. In these data there is a very rapid increase in activity and heart rate from a “winter mode” to “summer mode”, a signal I define as the end of the “hunger gap”. I hypothesize a relatively large annual variation in the date of this switch, cause by annual variation in snow melt, spring temperatures and plant growth. On the individual level I will investigate the effect of body condition, age and reproductive status on the date of the “end of hunger gap” signal. I will be provided with activity and heart rate data from my supervisor, and will myself collect the required data on ground spring temperatures and calf at heel status together with a field assistant in august 2024.

From winter time to summer time – what ends the "hunger gap" for Svalbard reindeer?

AILARON utviklet et nytt mobilt robotverktøy: et lett autonomt undervannsfartøy (LAUV) utstyrt med banebrytende bildebehandling og analytiske evner. Dette avanserte systemet validerte konseptet med å karakterisere biotaen i den øvre vannsøylen ved å bruke intelligent prøvetaking om bord, rettet mot spesifikke mesoplankton taksa.

AILARON klarte å avbilde, behandle, analysere og klassifisere planktonbaserte bilder, noe som gjorde at LAUV kunne utføre intelligent målrettet prøvetaking om bord. Denne tilnærmingen representerte et betydelig fremskritt fra tradisjonelle metoder, som vanligvis involverte å samle inn vannprøver, for så å transportere og analysere dem på et laboratorium. Dette er ikke bare tidkrevende, men henger ofte etter de raske endringene som skjer i marine miljøer. Ved å dra nytte av de nyeste fremskrittene innen bildebehandling, robotikk, maskinlæring og kunstig intelligens, forbedret AILARON denne metodikken betydelig, og muliggjorde en sanntidsanalyse på stedet av mikrobielle samfunn i rom og tid.

Prosjektet var sterkt tverrfaglig, og broen mellom feltene Robotic Vision, Machine Learning, Artificial Intelligence og Control Theory ble etablert. En av dets fremragende egenskaper var koblingen av robotsynsbasert prøvetaking med maskinlæringsteknikker. Dette gjorde det mulig for det marine robotfartøyet å autonomt målrette seg mot spesifikke mikrobiologiske taxaer innenfor den fotografiske sonen - de øvre 75 meter av havet der sollys trenger inn - ved hjelp av et nytt kamerasystem. LAUV kunne deretter behandle bildene på stedet, kategorisere og klassifisere mikroorganismer gjennom dyp læring, og generere et sannsynlighetstetthetskart i tre dimensjoner. Dette kartet kunne deretter veilede LAUV tilbake til områder rike på arter av interesse, takket være et avansert AI-basert kontrollsystem. Behandlingskjeden ble designet for å være fleksibel, og tillate menneskelig inngripen via en kommunikasjonsforbindelse til kysten. Denne funksjonen gjorde det mulig for forskere å dynamisk justere LAUVs prøvetakingspreferanser basert på sanntidsfunn, noe som gjorde kjøretøyet tilpasningsdyktig for skiftende forskningsmål.

AILARON-prosjektet var strukturert rundt fire hovedarbeidspakker: Bildebehandling og Sensorfusjon, Robotic Vision og Machine Learning, Flytestimasjon og Kartlegging, og Kartlegging med AI-planlegging og -kontroll om bord. Denne omfattende tilnærmingen sikret at alle aspekter ved robotisk utforskning og analyse ble optimalisert for effektivitet og effektivitet.

Feltarbeid ble primært gjennomført i Trondheimsfjorden og Frohavet. I tillegg var AILARON en del av en større forskningsinnsats kjent som "Ocean Observational Pyramid". Et system av systemer som integrerer multisensor- og multiscale-tilnærminger - inkludert satellittfjernmåling, autonome luftbårne, overflate- og undervannsfartøy, tradisjonell nett- og vannprøvetaking, og avanserte molekylære teknikker - for å overvåke og analysere fytoplanktonblomster og økosystemhelse. Det er en helhetlig strategi for å forstå blomstdynamikk ved å lette modellvalidering og forbedre prediksjon og håndtering av skadelige algeblomster gjennom å knytte rom-tid-makroskopiske med mikroskopiske observasjoner. AILARON tilbød dermed observasjoner med høyere forstørrelse og demonstrerte vellykket potensialet for å integrere teknologi og biologi for å bedre forstå de komplekse dynamikkene i havøkosystemer, spesielt med tanke på de observerbare endringene på planeten vår.

AILARON will image, process, analyze, classify plankton-based imagery to enable intelligent onboard targeted sampling on an autonomous underwater vehicle (AUV). The end outcome will be a novel mobile robotic tool for upper water-column microbial biology. In particular, this highly interdisciplinary project will have substantial impact on the study of microbial time-series in ongoing studies of the changing planet. It will accelerate the time consuming process in asking -who is there- from traditional methods of obtaining water samples, storage, transport, microscopy and analysis while leveraging advances in imaging and robotics hitherto not accomplished before.

The proposed project targets fundamental research in the fields of Robotic Vision, Machine Learning, Artificial Intelligence (AI) and Control Theory. In particular, we propose to couple Robotic Vision based upper water-column sampling and Machine Learning techniques for exploration with a marine robotic vehicle to autonomously target specific microbiological taxa. The AUV will use a novel camera system to image microorganisms in the photic zone (upper 75 meters), process imagery in-situ, categorize and classify based on Deep Learning, generate a probability density map in X,Y and Z planes, and use an advanced AI based controller to return to the most highly correlated hotspots with respect to species of interest. The entire processing chain will be embedded and guided by a human expert (as needed) via a communication link to shore to potentially alter her sampling preferences dynamically making the vehicle to adapt on-the-fly. 

The project funds one doctoral and one post-doctoral fellow and is organized in 4 work packages:

WP1: Imaging and Sensor Fusion
WP2: Robotic Vision and Machine Learning
WP3: Flow Estimation and Mapping
WP4: Mapping with onboard AI Planning and Control 

Our primary experimental site will be the Trondheimfjord.

0 antall prosjekter

IKTPLUSS-IKT og digital innovasjon

AILARON Autonomous Imaging and Learning Ai RObot identifying plaNkton taxa in-situ

Tildelt: kr 9,5 mill.

Prosjektperiode: 2017-2024

Sted: Trøndelag - Trööndelage