Dragon09/odtu_chatbot huggingface dataset:
selective co2 reduction to co and alcohols without platinum or noble group electrodes

the electrochemical conversion of co2 to carbonbased feedstocks represents one of few technological routes capable of replacing fossil fuel derivatives despite substantial advancements however major challenges impair co2 electrolysis from matching its promise critically steady acidification of co2 electrolyzers during operation currently necessitates the use of iridiumbased anodes this is unacceptable from a cost and resource availability perspective more fundamentally while co2 reduction to co formate and ethylene has become highly selective the production of highenergy density alcohols with high selectivity has been elusive to overcome these barriers new scientific approaches are neededrecallco2 will resolve iridium dependencies and nonselective alcohol production in co2 electrolysis through a combination of novel electrochemical cell design and the development of molecular catalytic architectures which break existing fundamental limitations on the system design front i will micro manipulate reagent ionic and water fluxes to inhibit nickel corrosion pathways which presently necessitate iridium anodes this will be the firstever intrinsically stable co2 electrolyzer capable of using nickel anodes a second pillar is the conceptualization that strong electroniccoupling of metal complexes to metal electrodes can eliminate redoxcontrolled reaction pathways on molecular catalysts this counters decades of work using carbon electrodes as supports coupling with a metal electrode will delink electron transfers from a molecular catalysts oxidation states and fundamentally change catalytic behaviour that currently restricts reactions to 2 electrons thus co2 reduction products such as methanol 6 electrons and ethanol 12 electrons will become viable utilizing this counterintuitive approach i will push alcohol synthesis well beyond stateoftheart selectivity and reaction rates giving renewed promise for producing these compounds
linguistic and acoustic properties of infantdirected speech in hindilearning infants hindibabynet

recently it showed that the research in infant language acquisition is highly skewed towards english and other wellstudied indoeuropean languages therefore most of the evidence regarding ids helps in infants language development stems from the families living in weird western educated industrialized rich and democratic countries like the uk us germany france etc yet we know that parents communication with their infants varies across cultures regarding their gestures vocal emotions and speech because of these differences in parentinfant interaction infant language learning in weird countriessocieties can be assumed to differ from contemporary infant learning in the developing world consequently we cannot ascertain whether the contrast of ids with ads and its tailoring to infants language development found in weird countriessocieties can be generalized to nonweird countriessocieties hence to generalize the impact of ids on infants language development empirical investigations are required in contemporary developing societies like india which show what life has been like for most infants in the developing world the linguistic and acoustic properties of infantdirected speech in hindilearning infants hindibabynet project will provide a unique opportunity to gain better insights into infant language acquisition in indiaacross various socioeconomic backgrounds this study aim to provide cdi norms for hindispeaking group also this study includes collecting infant and adultdirected speech data for hindi speakers across different socialeconomic statuses in india in addition this study aims to find the acoustic features of infantdirected speech and their significance in early language development from an indian perspective and also utilize machine learning model to investigate the acoustic cues of ids that are useful in learning mechanism
machine learning for decision making under uncertainty

many important decisions are taken under uncertainty since we do not know the development of various parameters in particular the ongoing green transition requires large and urgent societal investments in new energy modes infrastructure and technology the decisions are spanning over a very long timehorizon and there are large uncertainty towards energy prices demand of energy and production from renewable sources such problem can be described as twostage stochastic optimization problems where we first decide which facilities to establish and then we have to schedule the productiontransportation for a stochastic demand using the given facilities if the decision variables are discrete such problems are extremely difficult to solve in this project we will develop a new framework for investment decision making under uncertainty based on a combination of machine learning and operations research instead of solving a complex stochastic optimization problem defined on a fixed set of forecasted scenarios we propose to use an iterative process we repeatedly generate new scenarios solve them using advanced optimization methods and find the corresponding investment solutions our novel way of optimization will use deep generative models dgms to generate small sets of scenarios matching the real distribution and use a guided local search process to select scenarios that properly reflect properties of the full set of scenarios the outcome of the iterative process is a palette of nearoptimal solutions which can be analyzed using data science methods to extract associations in investments outrank dominated choices and organize investments according to urgency knowing the full spectrum of possible choices opens up for a much broader discussion of investments while allowing soft constraints to also be taken into account this will enable a more transparent and inclusive decision process while ensuring wellfounded and more robust investment decisions
automated production process for nextlevel redox flow battery stacks and modules following a revolutionary different and costoptimised production approach

to meet climate targets the worldwide increase of renewable energies is inevitable while the increase of renewables in the past years was backedup with fossil fuelbased generators the future electricity generation systems will rely on co2free energy storage devices to bridge the shortfalltimes from wind and solar consequently efficient use of renewables goes hand in hand with costefficient and reliable longduration stationary storage systems the new markets for longduration stationary storage systems demand lowest cost per cycle lcos sustainability and safety these criteria are at best fulfilled by redox flow batteries vanevo httpswwwvanevodehome invented new components for various kind of redox flow batteries including future concepts vanevo patented a revolutionary different innovate battery stack assembly process with significant cost reductions and intends to automate the stack and module production for redox flow batteries
deciphering the seismic signature of aseismic deformation

earthquakes are a major threat to humankind causing damage above 500 billion usd and more than 400000 fatalities within the last 20 years nonetheless the generation of large earthquakes remains poorly understood recent research suggests that the key to deciphering this preparatory phase lies in the complex interplay of seismic and aseismic processes three event types are of genuine interest slow slip events sses which are episodic aseismic deformations lowfrequency earthquakes lfes a type of earthquake depleted in highfrequency energy and regular earthquakesa key factor limiting the understanding of the seismicaseismic interplay are incomplete lfe catalogs caused by the difficulty to detect these events therefore in this project i will develop a novel detection method for lfes building on recent advances in deep learning applying this method i will compile comprehensive catalogs for three regions northern chile nankai japan and nicoya costa rica these catalogs in conjunction with continuous geodetic records sse catalogs and seismicity catalogs will allow me to study the seismicaseismic interplay this will reveal physical driving mechanisms of the seismicaseismic interplay and give insights into the preparation of large earthquakes this will contribute towards the accurate assessment of seismic hazard and the preparedness for seismic eventsi will conduct this project at the universit grenoble alpes with a secondment at the massachusetts institute of technology my scientific background in interdisciplinary research between deep learning and seismology is complemented by my supervisiors anne socquet expert on aseismic processes and subduction zones and william frank expert in the detection and characterisation of lfes the project together with targeted training activities will refine my scientific profile and extend my skill set enabling me to define my independent research agenda and pursue a career in research
reading minds and machines

can we decode the training data of a deep neural network dnn directly from its parameterstraining data of dnns are assumed safe recent findings by us and by others indicate that this is not the case with severe implications on dataprivacy yet such findings shed light on why dnns perform so wellon a different front can we decode what a person sawheardthinks directly from their brain activitythis may have huge benefits communicate with lockedin patients explore dreams manmachine interfaces enhance our understanding of the human brain no risk of violating human privacy here as thoughts do not float in the air and persons collaboration is requiredeach of those 2 questions is intriguing on its own with farreaching implications despite the inherent differences between human brains dnns they also have much in common exploring the two intandem can lead to significant breakthroughs in both fields recent advancements in both areas with recent incorporation of deeplearning dl tools to analyze brain activity opens the door to explore the two jointly our expertise in both domains will enable explicit encodingdecoding between brain activity dnn activations allowing to directly learninfer from one about the other initial explorations indicate that our proposed goals although ambitious are within reach our intermediate goals in each domain are worthwhile on their own forming a strong safety netexpected outcomes includedeep dataprivacyinsights on dnns their generalization vulnerabilitiesinsights on of what is encoded where in the brainnew scientific tools for brainscientists to explore the brain allow lockedin als patients to communicate their thoughtsneedsuse brain scanning to improve dnns dnns to improve brain scanningour project requires no human subjects nor brainscience expertise only publicly available datasets all methods lie in computervision dl with impact on both dl brainscience
solving differential equations fast precisely and reliably

being a language of nature differential equations are ubiquitous in science and technology solving them is a fundamental computational task with a long and rich history applications usually require approximate solutions which can be computed using numerical methods such as rungekutta schemes alternatively one may search for symbolic solutions which have the advantage of presenting the solutions in an exact and more intelligible way however such solutions do not always exist and may be hard to computethe present proposal aims at making the resolution of differential equations both faster and more reliable we will undertake a systematic analysis of the cost to compute both numeric and symbolic solutions as a function of the required precision special properties of the equation and its solutions and hardware specifics of the computer this includes the cost to certify approximate numeric solutions eg through the computation of provable error bounds in order to compute symbolic solutions more efficiently we will develop a new theory that relies on two techniques from computer algebra that were improved significantly in the past decade numerical homotopy continuation and sparse interpolationtheoretical progress on the above problems will be accompanied by open source implementations for this purpose we will also implement several high performance libraries of independent interest nonconventional medium precision arithmetic reliable homotopy continuation sparse interpolation fasterthanjustintime compilation etc altogether these implementations will validate the correctness and efficiency of our approach they should also allow us to tackle problems from applications that are currently out of reach
photosynthetic activity in low microalgal density suspensions

photosynthesis is at the intersection of many vexing problems facing humanity including feeding an everincreasing population securing the agricultural economy replacing fossil fuels with renewable energy and responding to climate changes studying this fundamental process and exploring its functional biodiversity is a crucial necessity indeed photosynthesis is a perfect tool for environmental monitoring in a context of climate changes it probes in a noninvasive way the physiological state of a photosynthetic organism its response to the environment moreover the discovery across diversity of new response mechanisms to environmental cues new metabolic pathways or regulatory mechanisms also constitutes a reservoir for biotechnological mining or crop improvement indepth measurements of plant photosynthesis are now possible in the field with the development of dedicated portable instruments combining chlorophyll fluorescence and absorption difference spectrometry however such approaches do not exist in the aquatic world where most of the diversity of photosynthetic organisms is yet located because the sensitivity of absorption difference spectrometers is not sufficient for natural samples characterized by very low concentrations of microalgae furthermore a very small proportion of microalgal species are currently cultivated and of these very few are lab rats reaching high cell concentrations for all these reasons a huge part of photosynthetic biodiversity escapes us in this project we propose to develop a high sensitivity absorption difference spectrometer which will allow measuring photosynthesis in diluted samples of one or more microalgal species we believe that the development of such an instrument will constitute a real breakthrough in the study of photosynthesis allowing to explore the diversity of architectures pathways and regulations of aquatic photosynthesis
multipurpose physicalcyber agriforest drones ecosystem for governance and environmental observation

he strategic objective of spade is to develop an intelligent ecosystem to address the multiple purposes concept in the light of deploying uavs to promote sustainable digital services for the benefit of a large scope of various end users in the sectors of agriculture forestry and livestock this includes individual uav usability uav type applicability eg swarm collaborative autonomous tethered uav governance models availability and trustworthiness multipurposes will be further determined in the sensing dataspace reusability based on trained aimachine learning ml models these will enable sustainability and resilience of the overall life cycle of developing setting up offering providing testing validating refining as well as enhancing digital transformations and innovation building services in forestry cropping and livestock farming pilot prototypes will contribute towards greater challenges such as deforestation precision cropping and animal welfare first spade will create a digital platform that is able to realise the potential benefits to be reaped from the use of drones this platform is making drone operations better accessible and controllable as well as providing a service channel for value added services enabled by drones second spade is demonstrating three innovative use cases of drones making use of the digital platform while demonstrating the use cases the benefits coming from the use of drones are analysed and quantified on a detailed stakeholder level basis this will demonstrate the new business opportunities the demonstrationspilots will also serve as an analysis platform to investigate the regulatory framework at international and national level open calls will provide 12 further use cases
stressinduced structural and organizational adaptations of the cellular translation machinery

many cellular and extracellular events cause perturbations of protein homeostasis by affecting either de novo protein folding or by destabilizing already folded proteins under such proteotoxic stress conditions cells engage in various strategies to avoid further damage to the cellular proteome eg by timely modulation of translation activity and specificity or by resolving the underlying events our overall goal is to dissect from a unique structural angle how such damage avoidance strategies impact on the structure and molecular organization of the translation machinery by directly imaging their effects on ribosome structure supramolecular organization and distribution in a cellular context with cryoelectron tomography cryoet an innovative imaging approach unique in its capability to provide highly detailed threedimensional structural information on macromolecular complexes in their cellular environment building on my pioneering work in the field of cryoet and integrating novel image processing solutions that have recently marked a breakthrough in the field we will dissect at unprecedented resolution how the cellular translation machinery is remodeled i after a general heatshock ii during the endoplasmic reticulum unfolded protein response and iii during persistent translational stalling triggering ribosomeassociated quality controlwork included in this proposal will provide detailed structural and mechanistic insights into how cells try to counteract an imbalance of protein homeostasis a hallmark of neurodegenerative diseases it thus seems almost certain that key concepts emerging from our studies will have direct implications on mechanistic understanding of central pathological principles underlying these diseases
chemical transformations in the interstellar medium photochemical processes

until the beginning of the last century the scientific convention was that the material in the interstellar medium ism between stars and galaxies was mostly in atomic and ionic form thus the formation of complex molecules was improbable today however large complex molecules are being discovered at a dazzling rate demonstrating the molecular nature of our universe the physical conditions in the ism are extremely different than on earth in addition to extreme temperatures and low density there are radiation fields which when interacting with atoms and molecules result in unique chemistry occurring in excited electronic states this interaction is responsible for many of the chemical phenomena observed in the ism currently very little is known about the formation mechanisms of molecules in the ism the efforts to uncover the chemistry of the ism are multidisciplinary yielding experimental observational and theoretical results theoretical results are crucial for obtaining a molecularlevel understanding of chemical phenomena theoretical results help to guide and decipher experimental and observational results however up to now the toolkit of modern quantum chemistry cannot model the dynamic of large molecules in a highly excited electronic state the aim of this proposed study is to fill this gap i will develop new theoretical capabilities within quantum chemistry specifically ensemble density functional theory that will enable us to model the dynamic of highly excited large molecules such as polyaromatic hydrocarbons which play a crucial role in the chemical evolution of the ism the ability to accurately model the dynamics of excited states will significantly advance the field of computational chemistry giving it the ability to model systems that are currently outside its reach and provide a leap in our current understanding of the chemistry of the ism
the doctoral network on prognostics and health management of next generation drivetrains

european manufacturing is at the centre of a twin ecological and digital transition being both driver and subject to these changes at the same time manufacturing companies must maintain technological leadership and stay competitive the size and the complexity of the associated challenges such as the integration of artificial intelligence the use of industrial data the transformation into a circular economy and the need for agility and responsiveness requires pooling of resources and a novel approach of cooperation the objective of the patron project is to develop the next generation of phm methodologies algorithms and technologies so enabling condition monitoring with the focus on realtime diagnostics and prognostics this objective will be achieved by having 10 doctoral candidates dcs working closely and interacting frequently in this interdisciplinary and multidisciplinary area despite remarkable progresses in health monitoring boosted by new technologies and ai most approaches still rely on the use of rudimentary his defined more than half a century ago on the other hand the community of tribology is working at the micro and the macroscale of the contacts where loads are applied and wear damage and faults occur impressively enough the two communities condition monitoringprognostics and health management and tribology are following separate paths the proposed patron project brings together the two communities and doctoral candidates and experienced specialists from key players in academia and industry across europe covering different scientific disciplines and industrial stakeholders from a broad range of backgrounds to optimally tackle the challenges ahead the patron fellows will be trained in innovative phd topics as well as receiving specific theoretical and practical education in the fields of mechanical engineering and computer science focusing towards the next generation prognostics and health management techniques
arabic linguistic discourse in the making

the alidim project proposes a metalinguistic analysis of the process of standardization of classical arabic as it has been described by early arabic scholarsby analyzing the earliest arabic grammatical texts under one metalinguistic roof this project aims to find new ways to account for and analyze texts that challenge conventional conceptions of the origins of the arabic linguistic tradition embedding linguistic production into a wider cultural framework the project introduces methodological innovations by devising an interdisciplinary approach the investigation will focus on the metalinguistic process of language description itself rather than on the linguistic features described with the objective to develop an unconventional new reading of the origin and formation of the arabic linguistic tradition the alidim project will explore a variegated corpus of arabic grammatical and lexicographical sources and picture the narratives presented by early arabic scholarsthe focus will be on identifying tracing and elaborating the earliest stages of formation of the arabic linguistic tradition through a reconstruction of the internal factors that gave rise to the emergence of the linguistic studies as well as the points of convergence between the arabic and neighboring traditionsthe alidim project will trace origins and receptions of the linguistic themes identifying the factors that altogether contributed to the process of language standardization of classical arabic the methodology applied by the early arabic scholars will also be studied with a twofold approach that accounts for both the local arabislamic framework and possible external influencesresearch data will be made available via the projects website as a digital corpus of teiencoded semanticallyannotated original sources