Dragon09/odtu_chatbot huggingface dataset:
characterization of the magnetism of exoplanets

the research in exoplanetary sciences has skyrocketed since the first discovery of planet 51 peg b by mayor and queloz 1995 more than 5200 exoplanets have been discovered since and have revolutionized our understanding of how starplanet systems are formed and evolve more and more exoplanets keep being discovered today and their characteristics and that of their host star are being precisely determined using spacebased and groundbased multiwavelengths observations nevertheless we are still blind to a fundamental aspect of exoplanets what kind of magnetism and magnetosphere do they possess on earth we know the magnetic field shields the atmosphere from the space environment and has been essential to maintaining life as we know it with the advent of the ska observatory we expect to detect magnetospheric radio signals from hundreds of exoplanets these magnetospheres also lead to starplanet magnetic interactions for about a third of the known exoplanets which leave observable traces on the activity tracers of the host star however such detections are not enough by themselves to quantify the magnetic properties of exoplanets due to an incomplete theoretical understanding of magnetic interactions in exomagnets we will therefore address the following science questions how planets interact magnetically with their environments how much energy can be involved and what observational tracers can be detected what type of magnetism can be sustained by an earth in the habitable zone of a lowmass star we will address these questions by filling out the present theoretical gaps about the magnetic coupling between an exoplanet and its local environment we will quantify for the first time its energetics its observable multiwavelength spectrum and leverage them to constrain the magnetism of exoplanets on shortperiod orbit these approaches will be combined to provide the theoretical tools to characterize the magnetism of a large sample of exoplanets
inflammatory signals of cell death

inflammation is a natural mechanism to restore tissue homeostasis and its deregulation causes human disease the programmed cell death form pyroptosis elicits inflammation in a cellautonomous and nonautonomous fashion by releasing cytokines and danger signals intriguingly immune pathology independent of cytokines has alluded to unexplored signaling circuits between cells regulating pyroptotic inflammatory reactionsfirealarm addresses the fundamental question of the physiological origin of inflammation using convergent systemwide organismal cell biological and molecular approaches we will test the central hypothesis that endogenous intercellular signaling proteins drive sterile inflammation and shift homeostatic stable to nonresolving chronic states we will determine paracrine activities of pyroptosis by systematic iterative ablation of molecule release from dying and perception pathways of sentinel cells in a complementary approach we will identify inflammatory signals in vitro and in vivo by newly developed cell typespecific mass spectrometrybased secretomics technologies holistic views of intercellular signaling proteins their exposure and modification will determine the molecular language orchestrating communication networks between cells and enable the recognition of signals initiating amplifying and resolving inflammation we will achieve a new level of molecular and organismal understanding of intercellular circuits governing homeostasis and conceive strategies to revert chronic conditions emerging inflammatory cell death markers will stratify molecular etiology and outcome of patients with sterile inflammatory diseases together firealarm tackles the fundamental principles of sterile inflammation relevant for understanding the pathogenesis of chronic metabolic and agerelated disorders
scientific insights into h2 combustion under elevated pressure conditions

insigh2t aims to advance the current scientific understanding regarding the effect of pressure on the turbulent burning rate thermoacoustic response and emissions performance of premixed hydrogen flames under relevant gasturbines operating conditions hydrogen with its high diffusivity and reactivity poses significant challenges to its clean and efficient utilisation as a fuel in gasturbines due to the lack of understanding of its pressuredependent turbulent burning rate crucial for combustion stability in gasturbines operation insigh2t leverages highpressure experimental measurements featuring advanced optical diagnostics coupled to cuttingedge direct numerical simulations focusing on a selection of simple canonical flames that are paradigms of more complex industrial burner geometries and configurations the fundamental insights gained will facilitate the development of advanced models and enhanced design tools empowering industrial oems to reduce the significant development time and costs of hydrogen combustion technologies by leveraging sciencebased predictive capabilities insigh2t aims to accelerate the deployment of clean reliable and efficient hydrogenfired gas turbines the projects impact extends beyond scientific understanding addressing directly relevant industry challenges crucially the involvement of two gas turbine oems ensures full alignment with the strategic research and innovation agenda of the clean hydrogen joint undertaking facilitating the swift transfer of improved combustion methodologies and understanding towards application in operational power plants ultimately insigh2ts contributions align fully with the objectives of the eu green deal reducing dependency on fossil fuels and offering a tangible pathway towards a more sustainable energy future
program intelligence declaratively and symbolically

the automatic understanding of programs in insightful highlevel terms has long been a dream of computer science the area of static program analysis has made significant progress in such understanding by algorithmically modeling all possible program behaviors in this setting declarative program analysis has recently demonstrated great success in capturing powerful algorithms efficiently and elegantly in a form that bridges mathematical logic and intuitive human understandingthe pis research has established a worldleading program in declarative program analysis with multiple independent signs of high recognition however the dream of automatic deep program understanding remains elusive static analysis tools are still reliant on significant human insights and extensive customization for the analysis domain is there hope for a giant step forward the pindesym approach posits that two emerging breakthroughs offer excellent promise to take declarative program analysis to a next level capable of realizing the dream of automatic program understanding the first is the idea of combining a declarative system eg a datalog fixpoint engine and a symbolic reasoning system such as an smt solver or algebraic rewrite system the second is the seamless integration of a machine learning approach over large amounts of data from past code bases in the declarative inference processthe pindesym project will leverage symbolic reasoning and learning approaches to greatly advanceprogram analysis the challenge is dual not only to invent powerful new techniques and algorithms but also to capture all the diversity in symbolic valueflow and learningbased reasoning in a single unified reusable and extensible analysis frameworka true deep program understanding engine far beyond current approaches
neuromorphic learning in organic adaptive biohybrid systems

artificial intelligence has demonstrated unprecedented advances in pattern and image recognition and is widely expected to significantly increase progress in smart healthcare devices but continues to rely on inefficient supercomputers operating remotely on the other hand relevant information for these applications mostly exists locally at the physiological level smart personalised bioelectronic applications can be tailored to a specific and unique case or person with the ability to be adapted trained and optimised over timein this erc project organic neuromorphic engineering is combined with bioelectronics to achieve a tuneable neuromorphic platform locally monitoring and modulating biosignals for the dynamic and adaptive learning control of a proofofprinciple soft robotic actuatordue to their compliant and nonlinear characteristics soft actuators are difficult to model and thus present an ideal opportunity to demonstrate neuromorphic learning control organic electronic materials have been successfully implemented as building blocks in neuromorphic computing and bioelectronic applications particularly mixed ionicelectronic conductors possess exceptional characteristics for use in biological environmentsat the interface between mechanical engineering materials science neuromorphic engineering and bioelectronics neurolabs will develop an organic neuromorphic platform by optimisation of organic materials and circuits and integration of sensors neuromorphic devices and microfluidics we will develop a closedloop adaptive biocircuit and demonstrate local tuning and neuromorphic learning control of a soft gripper finally we will show optimised biocontrol of the gripper using biohybrid synapses modulated by the neurotransmitter environment directly tuning the feedforward parameters in hardware this will open a completely new field of adaptive neuromorphic biointerfaces and inspire a novel conceptual approach for learning control
next generation vaccine platform leveraging skin immunity to provide diseasemodifying treatment of parkinsons disease

parkinsons disease pd is a major neurodegenerative disorder with no established treatment modalities capable of modifying disease pathology and no means of early diagnosis vaccines targeting asyn aggregates are a promising route to diseasemodifying therapy for pd but the current generation of pd vaccines utilise conventional formulations which are limited in their immunogenicity and require substantial quantities of adjuvant to achieve efficacy nexgens proprietary wisit vaccine platform is the first of the novel class of gluconeoconjugate vaccines gncvs which are administered intradermally and specifically formulated to leverage skin dendritic cells dcs to generate substantially stronger and more specific immune responses than conventional vaccines these stronger immune responses allow substantial reduction in adjuvants while simultaneously increasing therapy efficacy nexgen will identify and characterise candidate wisit constructs targeting asyn pdwisits and develop a novel extracellular vesicle evbased biomarker assay that enables early diagnosis of pd using liquid biopsies suitable for point of care use safety and efficacy of pdwisits will be demonstrated preclinically before being translated to firstinhuman phase iib clinical trials along with the novel evbased biomarker assay the results of nexgen will be the extraordinary accomplishments of cheap and effective diseasemodifying treatment of early pd and a novel biomarker assay to diagnose and guide prodromalearly pd treatment further still gncv technology will be clinically demonstrated which has the potential to be transformative to the treatment of a wide range of additional diseases resulting in farreaching impacts to the health of millions
climate resilient port infrastructure

clarion gathers a strong multidisciplinary team of 21 partners with complementary research technical and business profiles including four ports with inland waterway connections the top3 in europe in terms of container throughput namely rotterdam antwerpbrugge and hamburg in the north sea and constantza the largest european port in the black sea clarion ports handle 355 of the total container traffic in european ports as per eurostat published 2021 data and provide access to the major inland waterways of the rhinemaindanube axis scheldt and elbe ensuring significant impact of the application of results on the european maritime ports industry 10 pilot demonstrations focused on making port infrastructure and hinterland transport resilient and sustainable clarion pilot demonstrations will focus on going beyond the sota to test and deploy smart and sustainable quay walls monitoring and management system for the corrosion of port infrastructure with an automated floating mobile measuring system shore tension applicability for roro and conro terminals during storm conditions flood impact control dt dredged sediment reuse in port infrastructure nbs applicability in maritime ports dt to support the resilience of connected inland waterways infrastructure a dt for extreme weather forecasts federated learning using aerial dronessatellite datainsitu sensors for cadastral measurements and response to extreme weather and an ems for extreme weather events though each pilot demonstration will be carried out in one of the clarion ports achievements will be shared among them and beneficial results will be adopted in the shortterm international stakeholder communities will be engaged to create an open innovation ecosystem that will promote collaboration and sharing of experiences supported by an ab consisting of experts in climate resilience in ports to ensure that transferability of clarions results will not be limited among clarion ports
scientific exploitation of space data for improved ionospheric specification

dispec project aims at the exploitation of bottomside and topside ionospheric data provided by space missions such as swarm doris grace gracefo spire cosmic2 and by groundbased gnss receivers and ionosonde sounders to support research activities for improved ionospheric specification through the derivation of highlevel data products the project outcomes have the potential to complement the esa space science archives and the space weather network the data collections provided by the global networks of ionosondes and gnss ground based receivers and to enhance the capacity of european research infrastructures
strengthening democratic engagement through valuebased generative adversarial networks

generative adversarial networks gans are a class of ai models able to create media contents audio and video resembling reality although there are different promising areas of application of gans eg audiographic productions humancomputer interactions satire artistic creative expression their current and foreseen misleading uses are just as numerous and worrying the main concern is related to the socalled deepfakes fake images or videos simulating real events with extreme precision if trained on a face gans can make it move and speak in a hyperrealistic way this technology poses an urgent political threat since gans could be and have already been used to spread fake news and disinformationthis raises an urgent challenge to democratic governance and regulation to improve gans accountability transparency and trustworthiness nevertheless gans also constitute an opportunity to enhance democratic awareness and expand active and inclusive citizenshipsolaris reacts to these challenges in two ways on the one hand we analyse political risks associated with these technologies to prevent negative implications for eu democracies as a result solaris will establish regulatory innovations to detect and mitigate deepfake risks on the other hand we assess the opportunities raised by gans for reinvigorating the democratic engagement of citizens we will cocreate involving citizen science valuebased gans contents to enhance democratic engagement solaris involves three use cases the first aims at understanding the psychological aspects of gans perceived trustworthiness the second simulates the circulation of threatening gans contents on social media to detect risks and design mitigation strategies the third cocreates valuebased gans contents to boost awareness on key global democratic topics eg climate change gender dimension human migration to ultimately enhance active and inclusive digital citizenship
understanding corporate taxation

corporate taxation is a key area of policymaking it will be at the heart of many economic policies in the years to come to bring tax revenues or incentivize firms behavior in particular in the context of the european green deal however little is known about how firms react to changes in the corporate tax environment especially when it comes to microlevel evidence among all firms the reaction of large ones is of key importance because they account for an important share of economic activity and have more margins of adaptation than smaller firms in particular they can move their profits to tax havens to decrease their tax bill more empirical evidence is needed to understand how corporate taxation affects these firms to evaluate the impact of future reforms it is crucial to know how firms will react on different margins the level of production the wages they pay their investment the location of their profits knowing the elasticity of these variables to taxation is key to understanding corporate tax reforms and will contribute to knowledgebased policymaking the importance of these reactions cannot be overstated they affect public finance and the provision of public goods have macroeconomic impacts and have political and distributional consequences ucorptax aims to contribute empirically and theoretically to understanding large firms responses to corporate taxation to do so ucorptax will leverage a unique research design the exceptional additional taxation of large firms in france from 2011 to 2016 i will first merge administrative datasets providing precise firmlevel data on profits employment investment and wages i will then use frontier causal inference methods to explore firms responses to taxes on different margins finally i will build a theoretical framework to interpret the empirical results and provide additional results about the distributional effects of corporate taxes
revealing genes that mediate the cininduced interferoninflammatory response using a genomewide crispr screen coupled to a suicide gene switch

chromosomal instability cin and the resulting aneuploidies can fuel the dramatic karyotype changes characteristic of tumorigenesis helping tumors to alter the expression of immune checkpoints and evolving phenotypes related to immune evasion or stimulation antitumor immunity appears to be dependent on the type and origin of the tumor while immune evasion has become an obstacle to designing effective anticancer therapeutics understanding when cin promotes or suppress antitumor immunity is therefore needed however the mechanism and initiators by which this adjustment occurs and the role that aneuploidy cin or specific karyotypes have in this transition are still in the dark in order to understand how cin drives tumor evolution we need to know how cin guide cells into different immunogenic responses identifying the genes that adjust the role of cin and aneuploidy in immune evasion can therefore help us unravel the origins of malignant growth and in the future develop more appropriate therapeutic approaches my project plan is set to find the genes that trigger and connect chromosome missegregation to the interferoninflammatory response in cells suffering from cin using an unbiased genomewide crispr ko screen coupled to a suicide gene switch in human fibroblasts the use of a suicide gene switch normally used for cellbased therapy allows separation of cells that have lost the inflammatory pathway while the screen along with a next generation sequencing will find the genes related to that loss this screen will serve as a base to understand what underlies cancer immune evasion in chromosomally unstable cells
foreseeing the next generation of aircraft hybrid approach using latticeboltzmann experiments and modelling to optimize fluidstructure interactions

direct aviation emissions accounted for 38 of total co2 emissions and 139 of the emissions from transport in the eu in 2017 making it the second biggest source of greenhouse gas emissions after road transport in addition the growing amount of air traffic means that many eu citizens are still exposed to high noise levels intensified research and innovation activities are therefore needed to reduce all aviation impacts and emissions co2 and nonco2 noise manufacturing for the eu to reach its policy goals towards a netzero greenhouse gas emissions by 2050 one of the main levers to decrease co2 emissions is to reduce the airframe structural weight as an answer falcons ambition is to enhance the design capabilities of the european industrial aircraft sector focusing on fluidstructure interaction fsi phenomena to improve the aerodynamic performances of aircraft unsteady loads specifically falcon aims to develop highperformance predictive and multidisciplinary tools for fsi in aeronautics in order to reduce the aeroacoustics and aeroelastic instabilities using multifidelity optimization this will also benefit to specific noise emissions generated by flexible and mobile airframe structures when exposed to both low and highspeed fluid flowsto achieve its ambitious goal falcon assembles a unique interdisciplinary environment of fifteen public and private institutions and their affiliated entities from renowned research institutions to smes and aircraft hightier suppliers and integrators to cover all the required scientific and knowhow expertise building upon three industrial testcases and tight links with key european partnerships such as clean aviation falcon delineates a highimpactlowrisk proposal that will significantly contribute to the digital transformation of the european aircraft supply chain
establishing lensed supernovae as probes to address the hubble tension

lensed supernovae offer promising tools for cosmology with the upcoming revolutionary lsst survey however the current techniques to both find these systems and to properly use them to infer the hubble constant h0 are severely underdeveloped in this fellowship i propose pholos probing h0 with lensing of supernovae a threepart project to make lasting contributions to all steps of this process i will first solve the longstanding problem of finding and prioritising lensed supernovae by simultaneous exploitation of multiple datasets to find lensed supernova canidates without spectroscopic followup i will then address the issue of the mass distribution of lensing galaxies a recent systematic worry in all timedelay cosmography studies by modelling population level characteristics of a large sample of lensed quasars finally i will combine the new lensed supernovae and mass model constraints in a cosmological inference pipeline that will scale to the hundreds of systems with heterogeneous data quality expected from lsst yielding a precise and accurate measurement of h0 the timing of this project is ideal as lsst will start collecting data in mid2023 by expanding my knowledge and transferable skills this project will prepare me to be worldleading researcher thanks to new expertise on specific datasets like lsst and gaia on analysis techniques and machine learning methods and on astrophysical phenomena like supernovae gravitational lensing and galaxy mass profiles