Dragon09/odtu_chatbot huggingface dataset:
flexible open architecture and aidriven enabling technologies for a novel 6g connectivity platform

flecon6g gathers key participants from phase1 and phase2 sns ju projects aiming to make the intelligent 6g network of networks vision a reality with trustworthiness and generalisability of native ai and network digital twins ndt assisted approaches it aims to deliver key technological assets in the form of collaborative and distributed frameworks offering scalability and support for multidomain multistakeholder infrastructures jointly with network infrastructure programmability that supports the offloading of network application logic flecon6g provides groundbreaking research to drive the next phase of 6g native ai research addressing trustworthiness and generalizability via novel intentdriven frameworks and robust foundation models that not only deliver zerotouch orchestration but also performance guarantees and robustness in multistakeholder infrastructures a multilayer multivendor ndt framework will also assist online decisions and resolve conflicts among multiple closed control loops operating in reactive proactive or predictive mode for zerotouch selfoptimisation at the business layer a smart marketplace will facilitate the creation of a wide sustainable ecosystem of highly diversified stakeholders through which mnos digital service providers verticals and software developers of ai or dt software applications will jointly collaborate to build more integrated and intentdriven service offers composing a plethora of modular assets for their effective monetisation in line with eus policies flecon6g will design develop and validate through relevant pocs several secure trustworthy resilient and reliable solutions across 4 heterogeneous verticals that require 6g performance capabilities flecon6g will strongly impact standardisation with significant contributions to 3gpp itu etsi gsma and oran and opensource communities
connecting advisors toward a european network for consumerproducer chains

short food supply chains sfscs or producerconsumer chains are supply chains involving a limited number of intermediaries between farmers and consumers who are committed to cooperation local economic development and maintaining close geographical and social relations in rebuilding postcovid sfscs are being promoted by governments civil society and consumer groups in many eu member states as a means for building more inclusive resilient and sustainable food systems but sfsc initiators participants and promoters are confronted by a fragmented knowledge landscape coupled with a lack of clarity as to essential skills and competencies in all phases from farm to fork the corenet project proposes to respond by supporting the systematic development of more effective sfsc advising based on bringing together public private and civic advisors through itenabled peertopeer learning and mutual support this approach requires interacting with advisors farmers consumers and a more holistic approach aimed at helping sfscs achieve not just a bigger market share but a greener market impact project activities are concerned with 1 developing sfsc advisory capabilities based on a knowledge mapping and online listing of sfsc advisors from all eu27 in ways that enable the sharing and exchange of costeffective practice solutions that improve sfsc social economic and environmental performance and increase their impact in the food value chain 2 preparing learning resources in the form of a network of 30 golden cases exemplars of sfsc models that have achieved business success and initiating 27 lighthouse projects initiatives using golden cases to introduce advisory services for improving sfsc performance with at least one in each of the eu27 3 establishing a paneuropean peertopeer learning network for sfsc advisors and proposing a formal organizational solution that assures its sustainability and integration into the akis
hardware acceleration with tunable sramimc voltages

deep neural networks dnns are the fundamental component in most artificial intelligence applications with the increasing number of applications based on artificial intelligence the performance and energy efficiency of architectures running these algorithms have become crucial especially for batterypowered platforms in this work i propose an energy optimizing memory design framework with a special sraminmemorycomputing structure it also utilizes datapath optimization techniques like quantization and pruning with a finelevel assignment compared to other hardware accelerator studies for dnn processing in this work i will show that this special memory design together with the architectural datapath optimization techniques will have a much better capability of finding the pareto optimal point in the energyaccuracy tradeoff and increase the profitability of the final design
topoform robotic printing of largescale topology optimised composite construction formwork

concrete is a key enabler for creating an affordable inclusive and climateresilient built environment however the use of concrete contributes a massive 8 of global ghg emissions topology optimisation offers a pathway to reduce emissions now by using up to 70 less concrete to achieve the same structural performance while unlocking design innovation yet contractors typically opt for more affordable and predictable rectangular designs that require only standard formwork ie moulds panels resulting in a massive overuse of concrete the challenge to adopting topology optimisation is 3fold 1 typical timberbased specialty formworks are heavy and difficult to connect to standard formwork panels resulting in excessive and hazardous manual work to assembledisassemble 2 plywood tends to deform in moist conditions requiring costly onsite repair 3 formwork may be reused 720 times before being incinerated or landfilled in europe alone this generates 180m tons of unnecessary waste that emits 756k tons of co2e annually our topoform system is revolutionising global concrete construction by delivering specialty formwork at a radically lower price much faster to assembledisassemble onsite and upcyclablerecyclable through our proprietary takeback service topoform leverages our patented technology for robotic 3dprinting of lightweight composite formwork that is 52 cheaper 5x lighter and 30 to 60 lower embodied ghg footprint we have validated topoforms superior performance and customers willingness to pay with our first 2 leadcustomer projects in denmark since founding in 2012 odico as is an established pioneer in construction robotics we now aim to scale up our topoform formwork technology that will accelerate the new european bauhaus and eu green deal targets by 2028 odico will bring topoform specialty formwork to construction projects across europe and beyond reaching dedicated revenues of 825m and 80 total new employees
maps and graphs in mind and brain

how do our mind and brain represent the world in this project i propose that cognitive maps and cognitive graphs are two fundamental representational formats supporting our physical space and time conceptual taxonomic and thematic knowledge and cultural individualism and collectivism cognition to test this hypothesis i will combine stateofart neuroimaging techniques with innovative cognitive tasks including virtual reality to disentangle cognitive maps and cognitive graphs in both physical and conceptual domains i will also develop questionnaires and implicit behavioral tasks to assess individual preferences for map and graph representations and test whether this preference relates to the individual difference in cognition and culture this project has the potential to help trigger a paradigm shift from the traditional map perspective to a dualistic perspective which focuses on both maps and graphs thereby deepening our understanding of the organizing principles underlying cognition across domains this perspective could also inspire the current development of artificial intelligence and the characterization of individual differences in this project could in the long run guide policymaking design and educationthis project will be hosted by the max planck institute for human cognition and brain sciences which owns the most advanced neuroimaging facilities and a powerful network of servers for data storage and analysis prof christian doeller who is among the top cognitive neuroscientists in spatial memory and knowledge representation will be the supervisor this project will also open a new career avenue for the applicant by providing valuable opportunities to develop scientific and technical skills
engineering voicebased models and interfaces for enhancing the speech therapy of minimallyverbal children with autism and their communication

about 30 of children with autism are minimally verbal mv meaning they can communicate mainly through nonverbal vocalizations ie vocalizations that do not have typical verbal content vocalizations often have selfconsistent phonetic content and vary in tone pitch and duration depending on the individuals emotional state or intended communication while vocalizations contain important affective and communicative information and are comprehensible by close caregivers they are often poorly understood by those who do not know the communicator well an improved understanding of nonverbal vocalizations could pave the way for a better understanding of the cognitive social and emotional mechanisms associated with mv children with autism moreover it could lead to new therapeutic interventions for these subjects based on advanced voicebased technology for augmented alternative communication aac ie for communicating without using wordsthis msca touches on the research fields of audio signal processing in particular childrens vocalizations perception and humancomputer interaction in particular voicebased interfaces for speech therapy it aims to advance the understanding of mv autistic childrens vocalizations and exploit the obtained knowledge to create advanced voicebased interfaces to enhance their therapeutic interventions during the outgoing phase at mit the work will be about identifying and implementing machine learning algorithms for classifying childrens vocalizations the core strategy is to leverage the unique knowledge provided by caregivers who have longterm acquaintance with mv children with autism and can recognize the meaning of their vocalizations in the return phase at polimi the project will be about designing developing and empirically validating a voicebased aac prototype for childrens speech therapy through a participatorydesign process involving end users their caregivers and autism experts
designing molecular rotations in solid electrolytes for highperformance batteries

electrolyte materials are at the core of many technologies linked to green and sustainable future development such as batteries supercapacitors fuel cells electrochromic smart windows seawater desalination electrolytic hydrogen production research efforts in batteries have been focused on the dichotomy between the mutually exclusive advantages and disadvantages of solids vs liquid electrolytes here i propose an ambitious and innovative approach to achieve the best of both worlds through mesophasic inorganic plastic crystal electrolytes which feature solidlike macroscopic behavior but liquidlike local dynamicsa key feature of such plastic crystals is the rotational motion of the polyanions eg sulfate thiophosphate that is suspected to have a synergistic effect with the ionic migration paddlewheel or rotatingdoor effect of small cations such as li and na that are key to the function of electrochemical devices the goal of this project is to investigate this synergy between molecular rotations and ionic displacements at the atomic scale with the goal of developing more performant solid electrolyte materials for applications such as next generation batteries this is to be done by investigating the structure and dynamics of plastic crystals through combining my expertise in synthesis and diffraction with the host groups expertise in nmr and dft methodologies
ecoevolutionary dynamics in communities

hostparasite communities compose networks of interacting individuals of different species these communities are constantly coevolving ie reciprocal selective pressures coevolution produces ecological changes such as dynamics in demographies or in the distribution of interactions such ecological changes may feedback to drive further evolution in the communities however coevolutionary research usually focuses on isolated hostparasite pairs assuming that the ecological effects can be ignored similarly ecological studies argue that coevolution is not fast enough to affect ecological processes hence the reciprocal effect or ecoevolutionary feedback is generally ignored by both evolutionary and ecological studies when evaluating hostparasite interactions these assumptions possibly lead us to inaccurate understanding of the processes driving hostparasite communities and ecosystem dynamics in general i aim to infer and predict coevolutionary outcomes in communities of several host and parasite species from hostparasite interaction networks and demographic histories i will develop a unique combination of coevolutionary footprints in wholegenome sequences with dynamic network analysis ie the network changes the distribution of interactions according to selected genotypes over time i will use theoretical models and simulated data to link population genetics with interaction establishment and specificity at the ecological community level using the theoretical models i will finally build statistical inference tools to infer and predict changes in realworld ecosystems based on empirical genome and network data outcome brings together studies on genomic coevolution prof tellier tum ecoevolutionary dynamics in empirical communities dr möst uibk and communities as complex networks dr llopisbelenguer it will result in an enriching twoway transfer of knowledge and a complete development of the candidate as an independent researcher
implementation of an upgraded strategy to reach zero hiv transmission by breastfeeding in rural and urban settings in zambia the promisezero study

most cases of paediatric hiv now occur during the breastfeeding period through an edctp2 funded randomized controlled trial in burkina faso and lusaka we tested a postnatal prevention strategy relying on point of care poc early infant diagnosis eid and maternal vl tests for sameday infant lamivudine initiation when vl1000 copiesml until the end of breastfeeding and maternal art optimization at the 2nd immunization visit epi2 maternal hiv status was reassessed and infants from hivpositive mothers were enrolled among the 754 children in each arm the 12month transmission rate was 116100persyrs ci044260 in the control arm vs 0 975ci 071 in the intervention arm the period at risk for infant iehiv vl1000cml and no prophylaxis was 596100persdays 95ci 58561 vs 036 95ci 034036 the primary objective of the proposal is to assess the effectiveness of a similar intervention in 2 provinces of zambia to reach zero postnatal transmission for this implementation research proposal we will use the reaim framework we designed a cluster randomized controlled arm with 2000 hivexposed hivnegative children 1000 per arm recruited at epi2 visit in 40 clusters maternal child health centres matched for district and size the primary trial outcome is postnatal hiv infection at 18 months the eid and vl tests will be organised at the district level with a mobile team equipped with poc machines and one referral mch centre equipped with xpert platform research capacity will be built through phd fellowships by demonstrating the costeffectiveness of poc tests and infant lamivudine health technologies to prevent infant hiv infection identifying and addressing barriers to uptake in reaim notably via endusers participation and ensuring the translation of research findings into policy through the strong involvement of policy makers this study addresses both objectives of edctp3 and those of the present call
physicsinformed machine learning to accelerate stability research on perovskite solar cells

the development of sustainable energy production technologies such as new types of solar cells has traditionally taken decades these research cycles need to be accelerated in order to respond to the urgent climate change crisis perovskite solar cells pscs are a recent technology that boast high electricity production efficiencies however they suffer from insufficient lifetimes since there are thousands of material candidates even for a single layer of a psc it is challenging to search for stable materials and infer device degradation mechanisms this project aims to implement machine learning ml as a part of the solution i will create an accelerated mlassisted research cycle for improving the lifetime of pscs i will focusing on optimizing the composition of the light absorbing layer perovskite for stability i will show the resulting stability improvements in full pscs and develop faster inference of the remaining psc degradation mechanisms the acceleration in the cycle arises from i using ml to save experimental bandwidth ii designing experiments that are directly compatible with ml opposed to first collecting data and then figuring out how to use it with ml and iii maximizing effectiveness by teaching physics to the ml algorithm for example at the materials level the algorithm needs to know that certain perovskite compositions do not exist as mixed materials according to density functional theory the physics integration aligns ml algorithms with physical reality and alleviates data scarcity that has traditionally hindered the use of ml in experimental science my highly collaborative and interdisciplinary project demonstrates the potential of integrated mlassisted research cycles in accelerating stability research and develops applied ml methods applicable to optimization in both research and industry the project leads to more stable pscs thus taking us closer to sustainable energy production
an injectable and programmable drugeluting embolic device

this project aims to develop a novel platform for drug delivery and treatment for liver cancers a first in its kind twostep acting platform is described in detail depicting an early step of drug elusion to viable tissue in contrast to acute embolism followed by a later stage of vessel blockage to induce tissue necrosis is introduced here inspired by the coronary drug eluting stent concept we will introduce a novel porous degradable bead referred as embopore used as ministents that can elute drugs for predetermined time and collapse at the end of drug treatment period to embolize the tissue this twostep concept ensures that a proper and massive drug perfusion will be performed prior to occlusion of the blood vessel that is used to induce tumor necrosis another important advantage of the gradual blockage is in reduction of the secreted adverse procancer stress signals that occurs in the acute embolism procedure the product is designed as biodegradable and injectable to fit the current catheters as being used in the standard clinical procedures such product is expected to substantially improve the relatively modest clinical outcomes of embolic devices and possibly open new indications such as metastatic liver diseases gastrointestinal disease and more
certification for ethical and regulatory transparency in artificial intelligence

along the whole value chain in using data for economic purposes guidelines and tools are required to make the business of the different stakeholders successful and the endusers confident that none of their rights are endangered certain addresses these needs and delivers solutions for data holders dataspaces and ai systems providers and ai systems deployers which are the primary actors of the data and ai value chain they must be compliant with applicable european regulations must reach this compliance in a timely manner and at reasonable cost certain delivers guidelines and technical tools to help with compliance to assess data quality to measure biases in datasets and to protect privacy certain sets the foundation of ai certification it translates the regulations to business terms builds a directory of certification entities per business develops a platform to streamline the certification process and tools for ai system providers and certification entities so that they could respectively prepare and run a certification process in case of security breach not only privacy may get compromised but also ai models may become useless and lead to extremely damageable decisions to make sure that aibased products are of high quality and reliability certain develops security tools and methods specifically suitable for dataspaces and ai systems certain addresses the environmental footprint of the ai value chain innovative techniques are elaborated to reduce energy consumption when building and running ai systems this is beneficial not only for the green deal but to reduce cost for ai stakeholders as importantly certain considers the endusers perspective and provides templates and guidelines that may be used by ai systems deployers to reassure endusers on the use of their private data the project tests its results on seven operational pilots in six different business areas considering all the actors along the ai value chain
new perspectives on land dispossession violent conflict and migration in the west african sahel

farmers and pastoralists in the sahel continuously struggle to maintain control over land faced with rentseeking elites processes of modernization and uncertain land rights the hypothesis of landresponse is that smallholders who are dispossessed of access to land are more inclined to either migrate or resist violently violent resistance will be studied in this project with regard to the increasing numbers of armed groups labelled jihadist in the sahel and in particular in mali the project will investigate the nature of the links between land dispossession and elite capture on the one hand and violent jihadist resistance to governments as well as migration to north africa and europe on the other hand these hypothesized links will be investigated using mixed methods with a particular focus on mali as a hub country in the western sahel in terms of both violence and migration following this conceptual framework the objectives directing the project will be first to investigate whether smallholders in the west african sahel who are dispossessed of their land andor fall victim to rentseeking elites are more inclined to either resist violently or migrate and second try to assess why some smallholders are more prone to resist even violently while others remain compliant with its pioneering engagement with these urgent political issues the project will combine theoretical and empirical approaches from political ecology critical agrarian studies and peace and conflict studies to generate novel insights about the motivations and agency of smallholders confronted with emerging challenges to their livelihoods this interdisciplinary research will generate new knowledge on the interactions between land governance violent conflicts and migration which will both push the research frontier and provide new ideas for policy formulation