Dragon09/odtu_chatbot huggingface dataset:
multilingualism and sustainability in education in amazon trapezium

multiseat is a timely participatory research project aiming to expand the frontiers of multilingualism toward socioecological and environmental research to contribute to education for nature protection and sustainability focusing on an indigenous multiethnic and multilingual school in leticia amazon trapezium the project has three research objectives 1 design and implement multilingually a communitybased participatory methodology allowing the collection of socioecological and ethnographic interactional data about food supply systems fss deforestation def and ecotourism ecot 02 provide insights into the ideological stances perceptions attitudes beliefs and representations that the school community and their families have on these socioecological issues and 03 design a pilot of three researchbased multilingual inclusive teaching sequences mits to tackle fss def and ecot within the indigenous school to achieve these aims multiseat will empower young people from the school through participatory research engaging them in collecting quantitative and qualitative socioecological and ethnographic data analysing indigenous perceptions of environmental issues will provide insights into the complex relations between people language and nature also by considering language as a plurilingual praxis in the design of the mits we will focus on what people do and can achieve with their multilingual resources as ecolinguistic capital at project end the scientific community will have an empirically based comprehensive understanding of how the triangle peoplelanguagenature works we will also provide expertise and guidance to nonindigenous local and global communities policymakers smes interested in bioeconomy teachers privatepublic funders and stakeholders engaged in sustainability in amazonia in the implementation of interdisciplinary methods to operate changes through multilingualism education and environmental research
preventing humaninduced seismicity to fight climate change

climate change poses an imminent threat to our civilization prominent new technologies to fight climate change involve the earths underground renewable and sustainable energy resources and underground storage however all these technologies depend on the injection of fluids into the earths crust which in turn can cause significant earthquakes inject will solve this problem on the basis of a new groundbreaking scientific method that will prevent humaninduced seismicity and will maximize energy production and storage from renewable and sustainable natural resources injects interdisciplinary methodology is based on an astute scientific programme that brings knowledge far beyond the current state of the art it brings control theory and mathematics to the heart of this new challenging problem based on cuttingedge theoretical developments robust controllers and observers will be designed to optimally adjust fluid injection rates prevent induced seismic events over large regions and optimize energy production and storage the controllers will be derived using rigorous mathematical proofs and will take account of the complexity the heterogeneities and the various uncertainties of the underlying physical processes injects innovative theoretical methods will be thoroughly tested through novel numerical models and original experiments highfidelity numerical models will account for poroelastodynamics coulomb friction multiphysics and reducedorder modeling and will outpace any existing algorithms in fault mechanics both in terms of speed and accuracy the experimental plan will build on a novel laboratoryscale demonstrator and hybrid labcomputer testing that will be designed and constructed to experimentally validate injects new concepts only then will it be possible to apply injects methodology in practice and unlock the significant energy potential of the earth reduce carbon emissions and help save our civilization
imdea nanociencia doctoral training programme in nanoscience

ideal phd programme is a new doctoral fellowship programme promoted by fundacin imdea nanociencia imdea nano that aims to attract 12 talented researchers in two open calls to undertake excellent research in any of the programmes at the institutep1 nanotechnology for energy harvesting p2 quantum materials at the nanoscale p3 nanotechnology for healthcare p4 nanomagnetism for information and communication technologies p5 ultrafast phenomena at the nanoscale and p6 sustainable nanotechnologies and critical raw materials the research programmes are supported by a platform for disruptive innovation which encourages links with industry to help achieve translation of results to market provide support and facilities for prototyping proofofconcept testing and scalingup they will be offered secondments of 36 months at partner organisations relevant to their research careers both academic and nonacademic depending on preferencewithin the ideal phd programme we aim to boost the careers of talented researchers offering them an innovative truly interdisciplinary and intersectoral training in nanoscience and nanotechnology imdea nano is committed to providing a pool of researchers with the skills and knowledge needed to tackle the next challenges in the nanoscience fielddoctoral candidates will receive a wellbalanced research independence and mentoring from an experienced scientific and management team as well as access to an infrastructure of the highest level for the pursuit of their projects in nanoscience and nanotechnology fundacin imdea nanociencia is a nonprofit foundation created in 2006 jointly by the regional government of madrid and the government of spain to shorten the distance between research and society providing new capacity for research technological development and innovation in the field of nanoscience and nanotechnology
automated maskless laser lithography platform for first time right mixed scale patterning

laserbased technologies for creating structures in the range from nanometer up to millimeter size find many applications such as free form optics photonics multifunctional surfaces labonchip etc with a global market volume of 200 billion euros the original structures know as masters are the first step in the making of tools for keyenabling technologies like injection molding or nanoimprinting some of the current limitations in the laser lithography processes are the limited depth of the structures small area and low speed at process level highpower consumption in the laser interference lithography and multiple and expensive processes required for the development of hierarchical multifunctional structures at industrial levelthe optimal project will integrate for the firsttime different laser lithography technologies quality monitoring systems and processes in one platform for the development of structures with i high depth 150 micrometer ii dimensions in the range from 100 nm to submm in xyz iii 2d3d shape on flat surface iv combining parallel serial patterning v no need for external treatments on samples vi increased speed 1 cm2min and large area up to 2000 cm2 vii 40 of reduction in the consumption of resources for the whole manufacturing process the optimal project uses selflearning algorithms to optimize the virtual photomask as well as integrates methods for an inline control of the laser patterning by accelerating and upscaling the structuring process the optimal project will increase the process efficiency and yield which will reduce the energy consumption avoid material waste decrease costs and lead time in many applications the platform will potentiate the possibilities in the sustainable making of high quality versatile less costly masters for industrial manufacturing as demonstrated in 4 use cases optical lenses multifunctional riblet structures virtual reality lens microfluidic chips
optimal local algorithms via topologysensitivity

considering the rapid growth of data sets and network sizes over the past decade there is little doubt that the future of computation is distributed one central aspect lying at the heart of distributed algorithms is the question of locality what can be computed using only local information and how much information is needed while answering this question is essential for obtaining highly efficient algorithms for many important distributed problemssuch as load balancing in massive networks locality is a fundamentally important concept also in many other fields of algorithmics such as classical sequential algorithms massively parallel algorithms dynamic algorithms sublinear algorithms streaming algorithms or online algorithmsin the context of distributed algorithms a careful analysis of recent research on impossibilities reveals the key challenges in understanding localityand therefore in designing optimal algorithmsfor many graph problems understanding 1 how to solve treelike structures efficiently and 2 how to explicitly exploit any deviations from a treelike topology algorithmically the goal of this proposal is to gain a fundamental understanding of locality via the novel concept of topologysensitive algorithms that precisely addresses these key challenges by explicitly exploiting local topological features in the input instance such an understanding will result in optimal algorithms and tight complexity bounds for many important distributed problems thereby resolving central questions in distributed algorithms that have been open for decades laying the foundations for highly efficient and scalable algorithms in massive networks and implying significant improvements over the stateoftheart in a variety of other algorithmic fields in a nutshell the proposed project aims at a world in which everything that can be done locally will be done locally
from reconstructions of neuronal circuits to anatomically realistic artificial neural networks

artificial neural networks anns have found applications in a wide variety of realworld problems despite this tremendous success artificial intelligence systems still face major challenges due to their reliance on extensive training and large datasets recent reports indicate that the architecture of anns could be a prime target for reducing their training and data requirementswe hypothesize that such architectural features can be identified from neuronal networks in the brain which have evolved to efficiently perform highly specialized functions recent advances in electron microscopy will soon provide detailed reconstructions of largescale neuronal networks from different brain areas species developmental stages andor pathological conditions however even if such data become available directly transforming neuronal network reconstructions into anns will raise problems of interpretability due to their enormous complexity and generalizability due to high interindividual variabilityhere we will resolve these challenges by implementing a set of computational approaches that allow the extraction of rules that explain the wiring properties underlying dense connectomics data the transfer of these anatomical principles into the design of ann architectures and the evaluation of how these principles impact performance on a battery of deep learning tasks this unique methodology will lay the foundation for groundbreaking insights into how different network architectures facilitate specific brain functions and also how the underlying anatomical principles can inform the development of more effective and efficient artificial intelligence systemsour methodology will be publicly accessible online to scientists but also to companies and nonprofit organizations that seek to improve the performance or reduce training data requirements for applications of deep learning
molecular mechanisms of bacterial invasion and colonization in the human gut microbiota

the human gut microbiota plays a critical role in safeguarding human health by defending against pathogens and supporting essential metabolic and immune functions nevertheless our understanding of how the gut microbiota influences bacterial invasion and evolution within the human body is incomplete due to the complexity of these interactions furthermore the specific mechanisms through which commensal bacteria such as escherichia coli successfully invade and evolve in distinct human gut microbiota compositions remain unclear the microinvader project aims to address this knowledge gap utilizing an established in vitro gut simulator to investigate the precise mechanisms underlying the successful invasion of distinct human gut microbiota ecosystems by the commensal bacterium e coli furthermore microinvader aims to characterize at the molecular level the processes that shape the evolution of invading strains in response to specific human gut microbiota compositions subsequently microinvader will validate these findings through in vitro and in vivo experiments focusing on quantifying the selection strength acting on the genetic mechanisms that are crucial for bacterial invasion and colonization in different human gut environmentsby unveiling these novel mechanisms driving bacterial invasion and colonization within diverse human gut microbiotas microinvader seeks to provide critical insights that may have farreaching implications for enhancing human health ultimately microinvader aims to contribute to our understanding of how to manipulate the gut microbiota in ways that promote and sustain overall wellbeing
machine learningassisted simulation of metalloenzymes reactivity

metalloenzymes play a crucial role in various biological functions going from small molecule transportation to catalyzing essential metabolic ingredients understanding their reactivity is essential for advancing a broad range of fields and related industries such as medicine biotechnology environmental science and catalysis the computational modelling of biomolecules is a pillar of new drug and catalyst design but the common methods used for purely organicbased compounds such as empirical force fields cannot simply be applied in the presence of a metal centre due to their complex electronic structure on the other hand hybrid quantum mechanicalmolecular mechanics qmmm methods provide a way to study metalloenzymes but their computational overheads prevent their largescale use for molecular dynamics simulations and reactivity studies the project machine learningassisted simulation of metalloenzymes reactivity malamer aims to revolutionize the simulation of metalloenzymes reactivity by integrating machinelearning force fields with qmmm methods and advanced sampling techniques allowing for an unprecedentedly accurate description of their activity this innovative approach will lead to significant insights and applications in catalyst development and drug discovery with a potential impact on several eu priority areas the project will commence in september 2024 under the supervision of prof lunghi at trinity college dublin with a planned 3month secondment at university college london to gain additional expertise in advanced sampling simulations from prof salvalaglio a comprehensive set of training dissemination exploitation and communication activities are planned and they will be implemented with the help of the supervisors and trinity college dublins human resources training office and innovation and technology transfer office
enabling unobtrusive realworld monitoring of brainnetworks with wearable neurotechnology and multimodal machine learning

measuring and linking brain network activity to human physiology and behavior in natural everyday situations promises profound new insights into healthy brain function and disorders however the absence of suitable mobile neurotechnology presents a significant roadblock functional magnetic resonance imaging fmri has greatly advanced our understanding of brain function and networks but it is limited to singlesnapshot experiments in constrained lab settings electroencephalography eeg while mobile cannot directly be linked to brain networks captured by fmri to overcome these roadblocks and to advance neuroinspired treatments and discoveries to natural environments a hybrid wearable platform is required that combines innovations in hardware and analysis methods to enable continuous and stable measurements of brain network activity maps in the everyday world advancing highdensity diffuse optical tomography hddot can provide such a suitable alternative to fmri with a unique systems engineering concept integral aims to miniaturize and integrate dot eeg and physiological sensors with advanced multimodal machine learning to improve spatiotemporal contrast in mobile brainimaging to this end objective 1 instruments will develop hardware for unobtrusive and continuous wearable brainbody imaging with hddoteeg objective 2 experiments will collect extensive multimodal data for measuring brain networks while controlling for environmental and physiological artifacts objective 3 analysis will enable estimation of brain network activity with multimodal sensor fusion and machine learning and objective 4 integration will provide validation of robust brainnetworks imaging in ecologically valid everyday world environments if successful this new platform will provide unprecedented opportunities to study brain function with global impact on neurotechnology applications and research from neuroscience of the everyday world to digital health
patentpending cofermentation technology that mimics nature to create natural delicious and nutritious vegan seafood products

koralo creates natural delicious and nutritious vegan seafood through a patentpending cofermentation technology that mimics naturestarting with a beach walk in the spring of 2021 koralo was born out of the idea to create a sustainable food source inspired by algae washing up on shore sina albanese a serial sustainability entrepreneur with the desire to protect our oceanic ecosystems is supported by her father dr guido albanese with over 25 years of working experience in the food industry in this transgenerational startup koralo mimics with deeptech the best example we know nature in nature microalgae are consumed by small fish that are then consumed by larger fish which end up on our plates at koralo we have replaced fish with a unique cofermentation developed in germany where mycelia of edible mushrooms feed on microalgae to create a natural fish alternative productwe have already filed two patent families on this technology and have received positive assessment reports on both our european as well as global patent applications we have tested our products with chefs and consumers both in europe and asia and are expanding our range to salmon and shellfish such as shrimps and scallops koralo is partnering with the largest microalgae producer in europe in portugal for scaling up of koralos technology with their expertise in microalgae and fermentation and we have successfully reached trl 5 6 on existing equipment to launch our european production with them we have closed an mou koralo will launch its wholecut vegan fish in q1 of 2023 in b2b channels in korea with further expansion to singapore hong kong europe and the us within the next 5 years for this end we have closed several mous with restaurants and distributors in korea already our biotechnology will allow us to reach price parity with fish already at launch and grow koralo to a global company with revenues of almost 500 m within 5 years having a positive impact
inference in high dimensions lightspeed algorithms and information limits

extracting information from data is the key challenge of our time and in many applications eg genomewide association studies data compression and virtual assistants such as chatgpt both the data and the machine learning model used to extract information are increasingly highdimensional as traditional statistical theory is illequipped to face this explosion in the dimensionality of the problem machine learning is now predominantly experimental however empirical approaches come with huge costs affordable only to large companies and they lack interpretability which is especially troublesome in medical applications to address these issues the inf2 project develops informationtheoretically principled methods for highdimensional inference in machine learning and data science the key insight is that via a meanfield approach highdimensional quantities are well approximated by lowdimensional ones and then characterized exactly leveraging this characterization we will i establish the fundamental limits of inference ie the minimal amount of data necessary to solve the problem and ii design efficient algorithms requiring only the minimal amount of data the challenge we tackle is to apply this paradigm to practical settings in which data are structured and heterogeneous as in genomewide association studies and models consist of complex architectures tailored to applications autoencoders for data compression and transformers for chatgpt through a novel analysis of spectral methods approximate message passing and gradient descent inf2 builds a theoretical framework having conceptual impact as well as vast applicability in machine learning and information theory this framework is then brought to the real world via applications in genomewide association studies broadly our results enable the principled design of machine learning algorithms and models drastically reducing costs and providing interpretable solutions
scaling fluiddriven processes building collapse in extreme flow conditions

many earth system processes involving multiphysics multiphase conditions extend over several orders of magnitude in length and timescales engineering science in pursuit of deeper process understanding and solutionoriented design has used scaling theories to address scaleafflicted complex processes through experimental work in laboratory environment at reduced scale the standard scaling approach the buckingham theorem is especially deficient when multiphysics and multiphase processes require the choice of more than a single nondimensional number resulting in severe scale effects and typically meaning that accuracies at reduced scale are inadequately quantified hence we choose a demonstrably complex multiphysics multiphase process for the investigation of scaling accuracies the progressive collapsing of residential buildings and the associate debris transport evolving from extreme flow events from natural hazards such as flash floods or tsunamiangrywaters seeks to achieve a breakthrough in modelling these complex processes by deriving novel scaling laws that will be developed in the framework of the lie group of point scaling transformations scaling requirements will be applied to the combined fluidstructure interaction at various scales developing sophisticated building specimens here we employ 3dprinting and appropriately engineered materials to match the scaling requirements we conduct a comprehensive experimental campaign using medium and largescale facilities subjecting the specimens to extreme flow conditions in the form of dambreak waves we consider subassemblages single and multiple buildings enhancing the understanding of energy losses and debris production upon collapse elaborating reduced scale accuracies highfidelity numerical modelling will complement our experiments deepening our process understanding a depthaveraged model with novel debris advection model crucially enhances predictive capabilities