Dragon09/odtu_chatbot huggingface dataset:
a revolution in rotation sensing

rotation sensors also called gyroscopes are ubiquitous in consumer electronics navigation and environmental sensing the most advanced gyroscopes are ring lasers that are based on the sagnac effect all current compact and transportable devices however show significant drift and limited sensitivity which precludes their usage in fields of application where extremely small rotation rates in the nrads to prads range need to be measured these limitations are of purely technical origin they derive from residual movement of the gaseous laser medium light scattering and acoustic fiber noisewithin the scope of an erc starting grant we have implemented a disruptively different design of a ring laser gyroscope that circumvents these limitations now allowing for a compact and transportable device with nearzero drift and improved sensitivitysuch a device is in high demand for example in seismology where it would benefit earth quake and tsunami early warning systems sensing of environmental ground motion is imperative in the context of climate change monitoring the structural health of bridges and other largescale constructions is another pressing task where highly precise acquisition of rotation and distortion will have a massive impact on the early and reliable detection of structural fatiguewithin gyrorevolution we will demonstrate the supremacy of our concept and show operation outside of the laboratory we will develop an ipr strategy and prepare a patent application a detailed competitor and market analysis will constitute the first step on the pathway of deployment via a spinout company we will intensify contacts with companies to prepare for future partnerships importantly we will get involved with potential endusers earlyon to adapt our innovative technology to their needs gyrorevolution marks the first and decisive step in technology transfer from fundamental research to a scalable device with a wide range of applications
a holistic approach to bridge the gap between microsecond computer simulations and millisecond biological events

complete understanding of how complex biological objects operate and fulfill their cellular role requires at its core a detailed picture of the millisecond ms conformational transitions between functional states computational investigation of such msevents is thwarted by our difficultyand often impossibilityto identify and sample efficiently the relevant degrees of freedom at play as well as the current limitation of allatom molecular dynamics to the microsecond s timescale on common computer architectures guided by concrete biological questions that experiment alone has hitherto proven unable to address we propose a holistic approach to bridge affordable scomputer simulations and msbiological processes without the aid of a specialpurpose supercomputer to meet this grand theoretical challenge we will associate two powerful developments to make the quantum leap and open a breadth of applications scaling up to very large biological objects so far inaccessible to stimescale computer simulations first we will determine the reaction coordinate in an unprecedented combination of data driven discovery of collective variables and advanced algorithms to find the minimum freeenergy pathway that connects the end states of the conformational transition second we will accelerate sampling along this pathway by associating ergodic schemes to a novel approach that maps complex freeenergy landscapes significantly faster than its competitors we will apply this methodology to a v1voatpase a complete atpdriven biological motor that converts over the mstimescale the chemical energy of atp hydrolysis into mechanical work with minimalist dissipation beyond illuminating the allosteric pathways that underlie the conformational transition atomiclevel description of the rotarycatalysis milestones will shed new light on the effects of pathological mutations altering atp activity while helping engineer artificial cells with accelerated atp turnover
nanoscale advance of co2 electroreduction

the transformation of co2 into fuels and chemicals is one promising route to revert global warming enabling sustainable circular economies co2 electroreduction co2e offers a pathway to generate globally used chemicals using renewable electricity the performance of co2e towards key chemicals such as ethylene ethanol c2 and higher energydensity molecules increasing number of carbon atoms eg c3 and beyond is today far from its technoeconomic viability product selectivity energy efficiency stability and carbon utilization are when combined at scale insufficientfurther advances in co2r performance are precluded by the yet limited understanding of reaction pathways masstransport and reactant competition the scarce knowledge of the catalyst and its environment during reaction and the lack of ability to control these accurately at the catalyst metalliquid interface nascent tackles these challenges exploring an innovative catalyst design materials platform to address the catalyst interface through the atomic nano and microscales nascent exploits a family of metal and polymer precursors and engineers their assembly into metalpolymer interfaces with specific configurations tailored for co2e this is informed by a suite of complementary operando spectroscopies designed and orchestrated to resolve the unanswered questions of co2e interfaces at relevant operating conditions involving high current density abrupt potentials and a highly dynamic waterion environment this will enable the rational design of metalpolymer co2e interfaces that achieve and exploit control over composition reactants ion charges and electric fields across the needed spatial and temporal scales nascent will help answer key fundamental questions in co2e leading to transformative advances its ultimate goal is the viable clean electrosynthesis of most important c2 chemicals and a path to generate so far elusive c3 molecules efficiently
towards shared accountability rethinking the locus and scale of assessment and improvement in education

accountability has emerged as a central feature of educational governance worldwide however the prevailing accountability approach in education the socalled performancebased accountability pba has led to adverse effects and discomfort among key stakeholders in the education sector pba with its focus on individual schools as primary account providers intensifies competitive pressures fosters institutional fragmentation and often stigmatizes disadvantaged schools recognizing these drawbacks several governments are in the process of reevaluating their accountability frameworks this entails not only adjusting the measurement of performance outcomes but also giving greater attention to teachinglearning processes that were previously overlookeddespite these efforts ongoing reforms often overlook a crucial dimension the scale at which accountability practices occur accounted asserts that optimizing accountability requires not only a shift in focus but also a change in the locus of accountgiving practice from individual schools to broader school networks this recalibration of accountability emphasizing shared responsibility among schools within a network holds the key to fostering meaningful and cooperative relationships initiating sustained improvement dynamics and mobilizing additional educational resources at the local level the effective implementation of this approach necessitates a meticulous policy design that organizes accountability relations and practices within the context of school networks to bridge the gap between policy and practice we propose testing and refining the shared accountability model through a participatory design strategy involving real school actors this innovative approach by fostering collaboration and awareness among schools within networks holds the potential to bring about positive transformations across the entire educational system with a specific emphasis on advancing the wellbeing of disadvantaged schools
unraveling pacemaker dysfunction using novel stem cellderived human heart models

the pacemaker cells of the sinoatrial node san trigger three billion heartbeats in an average human life span at the start of each beat the san must generate enough current to drive the surrounding atrial tissue which contains 10000 times as many cells as the san itself this extraordinary biological feat is a result of intrinsic features such as san tissue architecture in addition san function is modulated by extrinsic inputs from the autonomic nervous system which adjusts the pace from beattobeat for example during exercise any disturbances to these core activities result in san dysfunction which include abnormal heart rate brady or tachycardias san exit block lack of atrial activation and chronotropic incompetence inability to increase heart rate san dysfunction is also a major trigger for atrial fibrillation the most common form of arrhythmiathe goal of this ercstg proposal is to make significant leaps in our understanding of the intrinsic and extrinsic factors that regulate san function in health and disease using innovative multi cell type as well as multi organ in vitro models created from human pluripotent stem cells the first objective is to determine the role of san subpopulations particularly transitional cells in facilitating impulse propagation at the pacemakeratrial interface by evaluating an scn5a lossoffunction mutation the second objective is to gain insights into the relationship between sandysfunction and atrial fibrillation by assessing myh6 variants associated with both these diseases lastly the third objective will unravel how damage to autonomic function in the higher orders of the brain for eg in neurodegenerative diseases such as huntingtons disrupts electrical activity of pacemaker cells this research will provide exquisite knowledge of the fundamental principles that regulate pacemaker function which is essential to effectively address san dysfunction and atrial arrhythmias
aiaugmented ecosystem for earth observation data accessibility with extended reality user interfaces for service and data exploitation

a vast amount of earth observation data is produced daily and made available through online services and repositories contemporary and historical data can be retrieved and used to power existing applications to foster innovation and finally improve the eu citizens lives however an undersizedaudience follows this activity leaving huge volumes of valuable information unexploited eo4eu aims to provide innovative tools methodologies and approaches that would assist a wide spectrum of users from domain experts and professionals to simple citizens to benefit from eo data eo4eu strives to deliver dynamic data mapping and labelling based on ai adding fairness to the system and data eo4eu introduces an ecosystem for holistic management of eo data bridging the gap among domain experts and end users bringing in the foreground technological advances to address the market straightness towards a wider usage of eo data eo4eu envisages to boost the earth observation data market providing a digestible data information modeling for a wide range of eo data through dynamic data annotation and a stateoftheart serverless processing by leveraging important european cloud hpc infrastructures
slovenian quantum science hub

located in ljubljana slovenia the joef stefan institute is the national leader in quantum research supported by the marie skodowskacurie actions programme the squash project slovenian quantum science hub will employ 40 foreign postdoctoral researchers the fellowships are designed to advance their research skills in four multidisciplinary fields quantum theory quantum materials quantum technology and quantum computing information with a broad range of international and intersectoral secondments as well as careerdeveloping training programs fellows will gain a blend of additional versatile skills significantly boosting their careergrowth opportunities the project aims at developing skilled and innovative quantum workforce across research technology and academia to tackle the upcoming challenges of the second quantum revolution
building models of fusiondriven sarcomas via cell fate engineering

one of the unsolved problems in cancer biology is why and how do certain oncogenic driver genes promote cancer in one cellular context but not in another this project focuses on fusion oncogenes relevant to sarcomas which are sarcoma typespecific and tumorigenic only in certain cellular states by combining cell fate engineering of human pluripotent stem cells hpscs with forced expression of sarcomalinked fusions i will systematically probe the cellular contexts and molecular mechanisms of fusiondriven sarcomagenesis with the prospect of building novel disease models that could be used for testing new therapies i hypothesize that fusiondriven sarcomas emerge from the combination of an oncogenic fusion protein and a celloforigin that is defined by a characteristic state prone to transformation i will investigate the interplay between a specific cellular context and the activity of a particular fusion and whether a permissive cellular context is sufficient for fusions to execute their tumorigenic programsi will pursue a build it to understand it approach and construct sarcoma models starting from hpscs inducible for the expression of five sarcomalinked fusions respectively i will validate these models against singlecell and spatial maps of sarcoma tumors available in the host lab i will exploit two complementary approaches for creating cells permissive to different fusion oncogenes i programmed differentiation of hpscs with precise molecular intervention in vitro objective 1 ii genetically engineered teratomas that could allow for full in vivo modeling of sarcomas objective 2alignment with the call innovative collaborative multidisciplinary project expected to qualify the applicant for a principal investigator position and an erc starting grant candidate is promising early career female scientist moving from italy to austria the candidate will bring her knowledge and new possible collaborations to the host lab
idriving intelligent digital roadway infrastructure for vehicles integrated with nextgen technologies

idriving aims to deliver a trl 6 prototype based on seven key pillars all designed to work synergistically to create an interactive accurate and efficient solution to improve infrastructure safety of urban and secondary rural roads i a safety criteria catalogue scc for all road users for secondary and urban roads this establishes a benchmark for safety standards and kpis forming the foundational blueprint that guides all aspects of the idriving system ii common communication solutions ensuring a seamless and realtime exchange of data across a diverse range of sensors from various vehicles and road infrastructures iii innovative sensor integration on compact packages these can be easily deployed and maintained on both vehicles and road infrastructure particularly on secondary and urban roads thus acting as core data collection or independent processing hubs for onsite analysis iv efficient and accurate monitoring services this includes visual inspection of the road network vehicle behavior analysis and the analysis of weather conditions all crucial for proactive road safety management v intelligence to maintenance operations to reduce the costs and impact on other users through advanced tools like 3d visualization defect detection at the local level and optimal traffic management vi aibased warning mechanisms that can provide realtime alerts about hazardous conditions or accidents either on road infrastructure with signs or lightning or directly to vehicle users via enhanced interfaces the final pillar ensures the seamless integration of all these elements bringing together sophisticated aidriven analytics machine learning sensor fusion and cuttingedge simulations into a vii digital twin of the road infrastructure this facilitates the anticipation identification and response to a wide range of road conditions and events in realtime
political process preferences in europe rethinking conceptual ontological and methodological foundations

to understand the levels of dissatisfaction with democratic performance in europe as well as the most effective reforms for addressing it requires an understanding of citizens process preferences how do they want democracies to function empirical research on process preferences is rapidly proliferating but it has been underpinned by an assumption that the task of this research is to discover which one of a set of abstract normative models of democracy citizens subscribe to the driving idea behind poppie is that this assumption is a serious limitation it has led the study of process preferences to focus on discrete choices between simple alternatives that fail to represent the real choices facing the inhabitants of complex democratic systems and neglected the extent to which individuals are conflicted between competing democratic norms it prevents robust inferences on important questions such as levels of polarisation and support for democratic innovationspoppie develops a groundbreaking new approach to rethink the conceptual ontological and methodological foundations for researching political process preferences first by applying democratic systems theory to reconceptualise the content of process preferences it will generate a wholly new conceptual understanding of preferences for complex democratic systems rather than ideal models of democracy second instead of assuming that the nature of process preferences is inherently ideological poppie uses a novel multidimensional theoretical framework to assess levels of contextuality conditionality and coherence in process preferences third it develops a new mixed methods approach producing a unique dataset that combines qualitative and quantitative data on a single individuals preferencethe new approach will not only strengthen the foundations of the science of process preferences but ensure its findings are more policy relevant for the project of renewing european democracies
superatom waveguide quantum electrodynamics

the past decade has seen remarkable advances in the field of quantum nonlinear optics where individual photons are made to strongly interact which each other such strong photonphoton interactions are of both fundamental and technological interest they are the prerequisite for implementing deterministic quantum logic gate operations for processing optical quantum information moreover photons that strongly interact via a quantum nonlinear medium exhibit complex outofequilibrium quantum dynamics that enable one to tailor and control the photon statistics of light quantum nonlinear effects have been successfully demonstrated with few photons in a number of experimental platforms which exploit resonant enhancement of emitterphoton coupling via highfinesse optical cavities collective response of ensembles of strongly interacting rydberg atoms socalled superatoms or efficient coupling of single quantum emitters to guided light in the realm of waveguide quantum electrodynamics qed however it remains a formidable challenge to reach the true manybody regime of quantum nonlinear optics where strong interactions and entanglement between many photons and many quantum emitters give rise to exotic quantum phases of light such as photonic molecules or fermionic subradiant states the objective of superwave is to realize this regime by synergizing superatoms and waveguide qed by uniting the expertise and experimental methods of three teams that have previously driven these fields independently we will develop nearideal fibercoupled nonlinear quantum devices their implementation will mark a major breakthrough in quantum optics and constitute a key resource in quantum sensing quantum metrology quantum communication as well as quantum simulations we will illustrate this great potential through a number of hallmark experiments such as the coherent fragmentation of a classical light pulse into its highly nonclassical photon number components
smart eventbased microscopy for cell biology

timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology however microscopes are passive systems and are still very limited in their operating capacity which limits in several ways our ability to identify and image complex biological events in real time and at the proper 4d scales they could become much more powerful investigation systems for the life science if they were endowed with userfriendly unsupervised decisionmaking algorithms transforming microscopes into fully responsive and automated measurement devices indeed we are at a moment when smart systems and artificial intelligence are being used everywhere including in laboratories to improve the functioning of many scientific however outdated preprogrammed microscopy workflows are still being routinely implemented the ability to employ realtime image analysis to inform optimize and adjust the settings of ongoing image acquisitions would be a game changer for studying complex dynamic cellular processes to address this issue we have developed a pilot software cyberscopy which enables the possibility to conduct image analysis in real time using deep learning to trigger modifications in the acquisition settings thus alleviating the need for manual input and supervision this allows for the implementation of novel classes of experiments that cannot be achieved with current solutions within the context of this poc cyberscopy will be developed into a userfriendly software capable of smart automation of microscopy systems an their addons eg microfluidics temperature controls etc as such cyberscopy has the potential to revolutionize the power and scope of microscopy experiments for quantitative cell biology with broad implications for the microscopy sector
datadriven agentbased models of investors with machine learning

image recognition or selfdriving cars are just a few among many applications of machine learning ml methods given that we can train a cobot to mimic human behaviour why not train a computer to mimic and simulate investor behaviour in stock markets this would not only improve understanding about investor decision making and their interaction but provide effective tools to predict investor behaviour on the microscopic level and simulate stock markets on the macroscopic level the main objective is to create a datadriven agentbased model abm where agents behaviour is governed by ml such models need appropriate data to be trained which is possible thanks to a unique big data set on investor level data accessible through the host the objectives are i framework for datadriven abm ii interpretable ml for abm iii verification of the interpretability of datadriven abms using synthetic data iv training the datadriven abms using actual shareholder registration data and finally v analysis of investors decisionmaking mechanism the objectives will be reached by using ml methods that achieve intrinsic interpretability with and without deep supervised learning this research requires a strong numerical skills and experience with simulations b computer infrastructure allowing to carry out largescale numerical analysis for which the fellow and the host have complementary experience the results will bring us closer to understanding the behavioural mechanism of market participants the project does not just gain understanding but introduces a datadriven approach to more realistic agentbased modelling which is completely new the outcome should focus the attention of regulators and policy makers who are often unable to realistically predict the effects of considered economic measures finally the project contributes to the ml literature on verification of interpretable methods with extensive data sets