Dragon09/odtu_chatbot huggingface dataset:
onchip quantum absorption spectroscopy in the infrared

116 of world deaths are related to air pollution the first step to tackle this problem is mapping the air quality with high resolution on a large scale this task requires a distributed network of miniaturized inexpensive and efficient gas sensors not available today in our project we aim at demonstrating a new quantum sensing measurement scheme on a photonic chip this would enable low cost mm2 footprint and efficient gas detection suitable for distributed sensing networksthe quantum process at the basis of our sensing proposal is called undetected photon quantum spectroscopy upqs where entangled photon pairs are used one of them in the visible vis while the other in the mid infrared mir range upqs exploits the indistinguishability between two sources of entangled pairs by measuring the visible photon one can realise mir absorption spectroscopy without the need of measuring the mir photon in this way it is possible to overcome the limitations of mir technologies while having access to the nonpareil spectral information of the mir range where molecules exhibit unique and strong vibrational absorption bandsthe proposed project will demonstrate upqs on a silicon chip for the first time by leveraging the maturity of cmos technology and silicon platform the new upqs scheme would enable a new class of sensors integrable on smart devices and accessible to the scientific and technology community at large the vision is to enable new devices for a wide range of applications from industry to medicine and to make possible a new approach to distribute sensing for highly resolved air quality monitoring moreover the demonstration of onchip upqs would provide new insights into integrated quantum photonics mir photonics and quantum sensing developing a platform for fundamentally new lines of research in fundamental and applied quantum photonics
investigating the manipulation of alignmentactivity via geometrical interaction effects in liquid crystals

controlling the orientation of rodlike objects is crucial for liquid crystal technologies and the natural world for biological systems recent studies have shown that living materials form liquid crystalline structures that dictate biological function these systems are outofequilibrium converting energy into motion and are modelled as active liquid crystals however alignment techniques commonly used in liquid crystal technologies are yet to be leveraged for active counterparts the prevailing chemical alignment methods do not scale to these larger system sizes geometrical orientation of rods however applies across length scales though the influence of geometrical features on alignment is not well understood even for passive systems imaginelc will investigate the manipulation of anchoring and activity via geometrical interaction effects on an experimental colloidal liquid crystal system the colloidal scale minimizes chemical interactions and enhances geometrical effects i will develop a passive and active model system that is confined by walls with diverse topographical features the project will be conducted at utrecht university where the host has expertise in confined liquid crystals and colloidal synthesisthe host and i will have a twoway transfer of knowledge by employing host expertise in liquid crystal analysis colloidal synthesis and highresolution imaging for this project these tools are essential for developing a colloidal liquid crystal my knowledge of molecular liquid crystals is complimentary to the host groups research focus on liquid crystal assembly my experience with complex liquid crystal device fabrication will complement the hosts body of knowledge by combining our expertise imaginelc will advance the fundamental understanding of geometry on liquid crystal alignment and dynamics establishing principles that are generalizable to synthetic and biological systems ranging from nanometric to millimetric sizes
scale business and technology to impact on disease research by with lossless and efficient data analysis

arent we expecting from disease research that all available data is being used yes yet one of the biggest hurdles is the data variety when to studying underlying mechanisms or prioritizing targets for novel treatments ultimately stalling projects and resulting in low output and quality knowing01 gmbh is a deep tech software company founded in 2020 in munich germany that gives life scientists full control of all their research data dr nikola müller founder and ceo has spearheaded the project since 2014 when the idea budded during her academic research at helmholtz munich the knowing01 solution is a softwareasaservice saas suite that allows the user to connect and explore omics data no matter their biodata type our innovative knowledgegraph based translation approach empowers scientists to run analyses across data types and compare different sets in their biological context we additionally offer on client premise licensed software our next challenging milestone is to scale a by innovating the business and b by having a critical assessment of the technology we aim to exploit our niche market position we are specifically aiming to be attractive to key partners that offer our together with their own services we need scale our infrastructure and databaserelated technologies alongside with the business only both will allow us to futureproof our business paving the way for a sustainable business dr nikola müller will profit from being part of the women leadership program by sharpening negotiation skills learning how to improve public visibility and finally getting into potent femalefriendly investor networks compared to classical it biotech or digital health companies our offering opens a yet unexplored niche which is why we will enormously profit from the women techeu grant the nondilutive funding will be critical of us being able to support the scaledevelopments to be able to move our business forward
proteomics for heart disease risk prediction

cardiovascular disease cvd remains the leading cause of death globally for men and women stateofthe art clinical risk prediction models for cvd use conventional risk factors and polygenic scores pgsa measure of an individuals inherited cvd risk however the accuracy of current models is moderate and worse in women than men protein markers of cvd represent an individuals current health state and both inherited and environmental disease risk indeed there is evidence that combining conventional risk factors pgs and protein markers of cvd may facilitate vast improvements in cvd risk prediction furthermore by modelling inflammationspecific protein markers missing from sexspecific cvd risk models protecthearts will specifically capture excess risk in womenprotecthearts will use the worlds largest populationbiobanklinked proteomics datasets to 1 develop a novel proteinbased risk score protrs 2 develop an inflammationspecific protrs iprotrs and 3 estimate the clinical utility of these scores when modelled with clinical risk scores and pgs cuttingedge machine learning algorithms will be used to establish target proteins for cvd prediction in 100000 individuals across four european biobanks protecthearts brings together a physicianscientist supervisor with proteomics expertise and a fellow with experience modelling cvd risk prediction using pgs in biobanks through a secondment and nonacademic placement the fellow will gain intersectoral experience activities of the training and dissemination work packages will build her competencies in machine learning advanced statistics and science communication protecthearts will engage clinicians in exploitation of the new sexspecific comprehensive cvd risk prediction model protecthearts has the potential to move cvd risk prediction beyond stateoftheart by improving risk prediction accuracy specifically in women using proteinbased markers for cvd and cvdrelated inflammation
neural basis of zebrafish collective decisionmaking

it is challenging for isolated animals to reliably extract and integrate behaviorally relevant information from the natural environment due to the limited sensory capacity of individuals many animal species therefore share and evaluate cues collectively allowing them to solve complex decisionmaking tasks as a group the behavioral algorithms and neural mechanisms that give rise to these cognitive abilities remain poorly understood in many fish species these behaviors are largely visionbased providing the opportunity to decipher the underlying general computational principles under wellcontrolled experimental conditions in the lab at the same time it is becoming possible to employ powerful neuroscientific techniques enabling new detailed analyses of the neural circuitry that orchestrates behavior i propose to establish the juvenile zebrafish as a model system that is optimally suited for the study of collective decisionmaking at this intermediate developmental stage zebrafish offer an excellent compromise between cognitive ability and experimental accessibility they can temporally and spatially integrate information they start to socially interact and one can characterize and manipulate brain activity in intact behaving animals using closedloop virtual reality experiments i will initially dissect the algorithmic rules by which juvenile zebrafish make decisions when swimming in heterogeneously biased groups i will then characterize brain activity related to this behavior in freely swimming fish and in restrained preparations finally to causally link neural circuit function and group decisionmaking performance i will carry out targeted laser ablation and optogenetic activation experiments thus my proposed research in juvenile zebrafish will for the first time provide key insights into the behavioral algorithms and neural mechanisms of how individual animals and animal collectives acquire sensory information and make complex decisions
experimental supercritical electrolyser development

xseed aims at developing an innovative alkaline membraneless electrolyzer that works at supercritical water conditions 374c 220 bar generating highquality h2 at pressures over 200 bar this technology maximizes energetic efficiency improves circularity and enhances lifetime resulting in a more competitive green h2 production xseed validates results at laboratory scale trl4 for a single cell and a 5cell stack novel catalysts and electrodes are designed synthesized and characterized to ensure high efficiencies multiscale modeling and cell design ensure laminar fluid flows allowing h2 and o2 separation without a membrane supercritical conditions and membraneless configuration reduce the electrochemical work required to generate h2 as interface resistances across cell components are decreased and increase system lifetime this results in an improved voltage and energy efficiency 42 kwhkg h2 current density 3 acm2 h2 production rate and robustness degradation rate 11000h xseed also integrates circularity and sustainability assessments in decisionmaking limiting the use of critical raw materials below 03 mgw and using wastewaters both for catalyst production and as a possible electrolyte for the supercritical electrolyser xseed consortium possess extensive technical knowledge and experience in key enabling technologies and areas these will be utilized to realize multiphysics models of cell and stack dtu snam idn pmat manufacture and select the best catalyst and electrodes leitat pmat idn and design the cell the stack and the test bench to validate the supercritical electrolyzer at a laboratory scale idn pmat snam in conclusion xseed projects relevance and added value extend beyond the technological dimension it will accelerate the h2 ecosystem supporting europe in meeting climate targets and maintaining its leadership position as a technological developer producer and exporter of green energy
implementation and validation of a closedloop neural interface to entrain brain rhythms and reduce motor symptoms in parkinsons disease

neurological diseases such as parkinsons disease pd are the leading cause of disability in the world the role of brain rhythms has not been completely understood but evidence suggest that specific motor disorders may be linked to abnormal brain oscillations researching ways to modulate specific cortical motor rhythms could lead to discovering more effective approaches to treat patients with neurological disorders affecting their motor condition however our current understanding tools are insufficient to access and interact with this kind of neural activity noninvasive neuromodulation protocols such as transcutaneous spinal cord stimulation tscs are raising interest in recent years to induce changes in the central nervous system cns that can lead to functional improvement while the side effects and applicability limitations of invasive procedures are overcome better understanding the interaction between neuromodulation protocols and brain rhythms is crucial for the development of interventions targeting pathological circuits which can favour plastic changes and the restoration of impaired motor functions in rhytmods i will develop a noninvasive neuromodulation platform to apply closedloop stimulation protocols synchronized with brain oscillations decoded from the periphery with muscle recordings highdensity electromyography hdemg the platform will allow to explore the modulatory effects of brainoscillationsdependent stimulation of the cns to treat motor symptoms and its potential to entrain and disrupt neural oscillations linked to motor recovery in patients with pd this research has the potential to transform our understanding of neurological disorders with pathological brain oscillations and to develop new minimally invasive and costeffective technologies to treat the motor symptoms in neural disorders
lassie the firstever painless breath analyser for people with diabetes a breakthrough in blood glucose monitoring

today dogs are used to detect high and low blood glucose cancers and are tested for coronavirusthe new lassie breath analyser boydsense will put the power of a dogs nose into peoples hands and give them access to gentle health monitoringlassie starts with diabetes as it is an immense burden for 40 million people in europe current glucose monitoring technologies are underused because they are highly invasive expensive stigmatising and generate a lot of wastelassie will transform this situation by making measurements painless simple discreet and costeffective without any consumables by analysing volatile organic compounds vocs in the breath lassie will calculate blood glucose value within current accuracy standard as a result lassie users will test earlier in the disease and more frequently leading to better diabetes management and significant cost savings eic accelerator will put boydsense on track to become a global leader in noninvasive diabetes monitoring
researching stereotypes towards lgbtqia community members with multilingual natural language processing

the rainbow project addresses the pressing issue of stereotypes and biases directed at lgbtqia individuals online in an era where digital discourse shapes societal attitudes it is imperative to address the rising tide of stereotypes targeting queer people these stereotypes can escalate into hate speech and violence perpetuating discrimination rainbows objective is to preemptively counteract these stereotypes fostering a more inclusive and tolerant online environmentwhile much research exists on hate speech detection there is a significant scarcity of initiatives that focus on early intervention to prevent stereotypes from evolving into hate speech and violence the project seeks to fill this gap by pioneering advanced multilingual natural language processing nlp techniques specifically tailored to recognize and counteract emerging stereotypes and biased narratives in english spanish and italian this proactive approach combined with collaboration across linguistics nlp sociology and lgbtqia studies offers a comprehensive understanding of the issue and the development of tools to address it effectivelyfurthermore the findings of this project will play a pivotal role in the development of an educational toolkit specifically designed to raise awareness about stereotypes and biases directed at lgbtqia individuals this tool will be tailored for the age group that spends the most time online 1624 years old ensuring that the next generation is equipped with the knowledge and understanding needed to foster inclusivity and combat stereotypes in the digital realmessentially the rainbow project aligns closely with the objectives of the work programme aiming to establish a safer online environment for lgbtqia individuals while advancing the frontiers of multilingual nlp technology to combat online discrimination this research endeavor bears substantial potential in shaping a digital future characterized by greater equity and inclusivity
field trials beyond 5g

longterm towards evolved 5g and 6g research needs mobilization of massive volumes of ideas from which the best can emerge and be taken into standardization it is the convergence of the cyber and physical worlds that could be achieved with the very low latency and high data capacity of beyond 5g and 6g networks and thus the creation of services such as digital twins and internet of senseshaptic applications this future ecosystem is on course to be more collaborative and inclusive than previous generations of network and fidal aims to lead the way by promoting open architectures large experimentation sites and taking a multistakeholder approach fidal steps in targeting the augmentation of human capabilities allowing media ppdr vertical industry players to perform advanced technological and business validation in largescale field trials of highly innovative and advanced applications that exploit evolved 5g technologies this in turn will provide extensive and valuable feedback to the broader industry academia innovators and community before wide commercial deployments of evolved 5g networks throughout europefidal key objective is to extend and deliver i advanced future proof evolved 5g test infrastructures anticipating evolution into next sns phase ii open accessible to support 3rd party vertical experiments iii test environments for rapid prototyping and largescale validation of advanced forwardlooking applicationsthe project will build on the success of 5gppp phase 3 projects producing a unified experimentation framework with zerotouch orchestration reusable netapps and secure ai as a service capabilitiesfidal will deploy 3 testbeds and 3 largescale test infrastructures in greece norway and spain aiming to validate evolved 5g technologies in a user context to maximise downstream take up thus cultivating the ground for 6g
realtime inversion using selfexplainable deep learning driven by expert knowledge

indeep is a european doctoral network composed of nine doctoral candidates dcs and top scientists with complementary areas of expertise in applied mathematics artificial intelligence highperformance computing and engineering applications its main goal is to provide highlevel training to the nine dcs in designing implementing and using explainable knowledgedriven deep learning dl algorithms for rapidly and accurately solving inverse problems governed by partial differential equations pdesinverse problems in which the unknown parameters are connected to experimental measurements through pdes cover from medical applications like cancer growth assessment to the safety of civil infrastructures and green geophysical applications such as geothermal energy production their application value is measured in human lives and societys wellbeing which goes beyond any quantifiable amount of money this is why equipping a new generation of specialists with highlydemanded skills for the upcoming transition toward safe and robust aibased technologies is imperativedespite the promising results in many applications dl for pdes has severe limitations the most troublesome is its lack of a solid theoretical background and explainability which prevents potential users from integrating them into highrisk applicationsindeep aims to remove these constraints to unleash the full potential of dl algorithms for pdes we will achieve this by a focusing on emerging applications of dl for pdes with immense societal andor industrial value b designing mathematicsinfused advanced solvers to address them efficiently and c involving from the beginning industrial and technological agents which can monitor upscale and exploit this knowledge on the way we shall establish the foundations of a better knowledge exchange ecosystem amongst the main academic and industrial actors within europe disseminating the results worldwide
why do infants learn language so fast a reverse engineering approach

how do infants learn their first languages the popular yet controversial statistical learning hypothesis posits that they learn by gradually collecting statistics over their language inputs this is strickingly similar to how current ais large language models llms learn and shows that simple statistical mechanisms may be sufficient to attain adultlike language competence but does it estimates of language inputs to children show that by age 3 they have received 2 or 3 orders of magnitude less data than llms of similar performance and the gap grows exponentially larger with childrens age worse when models are fed with speech instead of text they learn even slower how are infants so efficient learners this project tests the hypothesis that in addition to statistical learning infants benefit from 3 mechanisms that accelerate their learning rate 1 they are born with a textitvocal tract which helps them understand the link between abstract motor commands and speech sounds and decode noisy speech inputs more efficiently 2 they have an textitepisodic memory enabling them to learn from unique events instead of gradually learning from thousands of repetitions 3 they start with a textitevolved learning architecture optimized for generalisation from few and noisy inputsour approach is to build a computational model of the learner an infant simulator which when fed by realistic language input produces outcome measures comparable to childrens laboratory experiments vocabulary estimates this gives a quantitative estimate of the efficiency of each 3 mechanisms as well as new testable predictions we start with english and french that have both accessible large annotated speech corpora and documented acquisition landmarks and focus on the first three years of life we then help building similar resources across a larger set of languages by fostering a crossdisciplinary community that shares tools data and analysis methods
sustainable and nature inclusive offshore energy with the parallel biodiversity flourishing protection and monitoring

sunbio brings together a significant variety of disciplines aiming at exploiting them to form a set of technology enablers that will facilitate the delivery of the envisioned services through a holistic framework comprising underwater engineering mathematics and analytics computer science archaeology chemistry further decomposed in i naval engineering and design ii trustworthy data analytics and relevant intelligence mlai frameworks iii chemical measurement and spectroscopic methods for sensing iv navigation principles and compliance v communication and remote operation these disciplines will be exploited to set up a number of complementary technology enablers also described in detail in sect121122 these enablers will be the basis for the sunbio envisioned services that will lead to efficiently applied fully autonomous and in situ sensing monitoring services sunbio aims at ensuring that the pathway towards robust monitoring principles and practices is designed always having human in the loop in terms of usability and acceptance of deployed technologies given the fact that trustworthiness is a key factor towards the exploitation of the envisioned set of services