5,599 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Resilience and food security in a food systems context

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    This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners

    DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

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    This paper proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a sub-goal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move towards the goal. Through a series of 3D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized. Multimedia demonstrations are available at https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS.Comment: Accepted by IEEE Transactions on Robotics (T-RO), 202

    Application of Track Geometry Deterioration Modelling and Data Mining in Railway Asset Management

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    Modernin rautatiejĂ€rjestelmĂ€n hallinnassa rahankĂ€yttö kohdistuu valtaosin nykyisen rataverkon korjauksiin ja parannuksiin ennemmin kuin uusien ratojen rakentamiseen. Nykyisen rataverkon kunnossapitotyöt aiheuttavat suurten kustannusten lisĂ€ksi myös usein liikennerajoitteita tai yhteyksien vĂ€liaikaisia sulkemisia, jotka heikentĂ€vĂ€t rataverkon kĂ€ytettĂ€vyyttĂ€ SiispĂ€ oikea-aikainen ja pitkĂ€aikaisia parannuksia aikaansaava kunnossapito ovat edellytyksiĂ€ kilpailukykyisille ja tĂ€smĂ€llisille rautatiekuljetuksille. TĂ€llainen kunnossapito vaatii vankan tietopohjan radan nykyisestĂ€ kunnosta pÀÀtöksenteon tueksi. Ratainfran omistajat teettĂ€vĂ€t pÀÀtöksenteon tueksi useita erilaisia radan kuntoa kuvaavia mittauksia ja yllĂ€pitĂ€vĂ€t kattavia omaisuustietorekistereitĂ€. Kenties tĂ€rkein nĂ€istĂ€ datalĂ€hteistĂ€ on koneellisen radantarkastuksen tuottamat mittaustulokset, jotka kuvastavat radan geometrian kuntoa. NĂ€mĂ€ mittaustulokset ovat tĂ€rkeitĂ€, koska ne tuottavat luotettavaa kuntotietoa: mittaukset tehdÀÀn toistuvasti, 2–6 kertaa vuodessa Suomessa rataosasta riippuen, mittausvaunu pysyy useita vuosia samana, tulokset ovat hyvin toistettavia ja ne antavat hyvĂ€n yleiskuvan radan kunnosta. Vaikka laadukasta dataa on paljon saatavilla, kĂ€ytĂ€nnön omaisuudenhallinnassa on merkittĂ€viĂ€ haasteita datan analysoinnissa, sillĂ€ vakiintuneita menetelmiĂ€ siihen on vĂ€hĂ€n. KĂ€ytĂ€nnössĂ€ seurataan usein vain mittaustulosten raja-arvojen ylittymistĂ€ ja pyritÀÀn subjektiivisesti arvioimaan rakenteiden kunnon kehittymistĂ€ ja korjaustarpeita. Kehittyneen analytiikan puutteet estĂ€vĂ€t kuntotietojen laajamittaisen hyödyntĂ€misen kunnossapidon suunnittelussa, mikĂ€ vaikeuttaa pÀÀtöksentekoa. TĂ€mĂ€n vĂ€itöskirjatutkimuksen pÀÀtavoitteita olivat kehittÀÀ ratageometrian heikkenemiseen mallintamismenetelmiĂ€, soveltaa tiedonlouhintaa saatavilla olevan omaisuusdatan analysointiin sekĂ€ jalkauttaa kyseiset tutkimustulokset kĂ€ytĂ€nnön rataomaisuudenhallintaan. Ratageometrian heikkenemisen mallintamismenetelmien kehittĂ€misessĂ€ keskityttiin tuottamaan nykyisin saatavilla olevasta datasta uutta tietoa radan kunnon kehityksestĂ€, tehdyn kunnossapidon tehokkuudesta sekĂ€ tulevaisuuden kunnossapitotarpeista. Tiedonlouhintaa sovellettiin ratageometrian heikkenemisen juurisyiden selvittĂ€miseen rataomaisuusdatan perusteella. Lopuksi hyödynnettiin kypsyysmalleja perustana ratageometrian heikkenemisen mallinnuksen ja rataomaisuusdatan analytiikan kĂ€ytĂ€ntöön viennille. Tutkimustulosten perusteella suomalainen radantarkastus- ja rataomaisuusdata olivat riittĂ€viĂ€ tavoiteltuihin analyyseihin. Tulokset osoittivat, ettĂ€ robusti lineaarinen optimointi soveltuu hyvin suomalaisen rataverkon ratageometrian heikkenemisen mallinnukseen. Mallinnuksen avulla voidaan tuottaa tunnuslukuja, jotka kuvaavat rakenteen kuntoa, kunnossapidon tehokkuutta ja tulevaa kunnossapitotarvetta, sekĂ€ muodostaa havainnollistavia visualisointeja datasta. Rataomaisuusdatan eksploratiiviseen tiedonlouhintaan kĂ€ytetyn GUHA-menetelmĂ€n avulla voitiin selvittÀÀ mielenkiintoisia ja vaikeasti havaittavia korrelaatioita datasta. NĂ€iden tulosten avulla saatiin uusia havaintoja ongelmallisista ratarakennetyypeistĂ€. Havaintojen avulla voitiin kohdentaa jatkotutkimuksia nĂ€ihin rakenteisiin, mikĂ€ ei olisi ollut mahdollista, jollei tiedonlouhinnan avulla olisi ensin tunnistettu nĂ€itĂ€ rakennetyyppejĂ€. Kypsyysmallin soveltamisen avulla luotiin puitteet ratageometrian heikkenemisen mallintamisen ja rataomaisuusdatan analytiikan kehitykselle Suomen rataomaisuuden hallinnassa. Kypsyysmalli tarjosi kĂ€ytĂ€nnöllisen tavan lĂ€hestyĂ€ tarvittavaa kehitystyötĂ€, kun eteneminen voitiin jaotella neljÀÀn eri kypsyystasoon, jotka loivat selkeitĂ€ vĂ€litavoitteita. Kypsyysmallin ja asetettujen vĂ€litavoitteiden avulla kehitys on suunniteltua ja edistystĂ€ voidaan jaotella, mikĂ€ antaa edellytykset tĂ€mĂ€n laajamittaisen kehityksen onnistuneelle lĂ€piviennille. TĂ€mĂ€n vĂ€itöskirjatutkimuksen tulokset osoittavat, miten nykyisin saatavilla olevasta datasta saadaan tĂ€ysin uutta ja merkityksellistĂ€ tietoa, kun sitĂ€ kĂ€sitellÀÀn kehittyneen analytiikan avulla. TĂ€mĂ€ vĂ€itöskirja tarjoaa datankĂ€sittelyratkaisujen luomisen ja soveltamisen lisĂ€ksi myös keinoja niiden kĂ€ytĂ€ntöönpanolle, sillĂ€ tietopohjaisen pÀÀtöksenteon todelliset hyödyt saavutetaan vasta kĂ€ytĂ€nnön radanpidossa.In the management of a modern European railway system, spending is predominantly allocated to maintaining and renewing the existing rail network rather than constructing completely new lines. In addition to major costs, the maintenance and renewals of the existing rail network often cause traffic restrictions or line closures, which decrease the usability of the rail network. Therefore, timely maintenance that achieves long-lasting improvements is imperative for achieving competitive and punctual rail traffic. This kind of maintenance requires a strong knowledge base for decision making regarding the current condition of track structures. Track owners commission several different measurements that depict the condition of track structures and have comprehensive asset management data repositories. Perhaps one of the most important data sources is the track recording car measurement history, which depicts the condition of track geometry at different times. These measurement results are important because they offer a reliable condition database; the measurements are done recurrently, two to six times a year in Finland depending on the track section; the same recording car is used for many years; the results are repeatable; and they provide a good overall idea of the condition of track structures. However, although high-quality data is available, there are major challenges in analysing the data in practical asset management because there are few established methods for analytics. Practical asset management typically only monitors whether given threshold values are exceeded and subjectively assesses maintenance needs and development in the condition of track structures. The lack of advanced analytics prevents the full utilisation of the available data in maintenance planning which hinders decision making. The main goals of this dissertation study were to develop track geometry deterioration modelling methods, apply data mining in analysing currently available railway asset data, and implement the results from these studies into practical railway asset management. The development of track geometry deterioration modelling methods focused on utilising currently available data for producing novel information on the development in the condition of track structures, past maintenance effectiveness, and future maintenance needs. Data mining was applied in investigating the root causes of track geometry deterioration based on asset data. Finally, maturity models were applied as the basis for implementing track geometry deterioration modelling and track asset data analytics into practice. Based on the research findings, currently available Finnish measurement and asset data was sufficient for the desired analyses. For the Finnish track inspection data, robust linear optimisation was developed for track geometry deterioration modelling. The modelling provided key figures, which depict the condition of structures, maintenance effectiveness, and future maintenance needs. Moreover, visualisations were created from the modelling to enable the practical use of the modelling results. The applied exploratory data mining method, General Unary Hypotheses Automaton (GUHA), could find interesting and hard-to-detect correlations within asset data. With these correlations, novel observations on problematic track structure types were made. The observations could be utilised for allocating further research for problematic track structures, which would not have been possible without using data mining to identify these structures. The implementation of track geometry deterioration and asset data analytics into practice was approached by applying maturity models. The use of maturity models offered a practical way of approaching future development, as the development could be divided into four maturity levels, which created clear incremental goals for development. The maturity model and the incremental goals enabled wide-scale development planning, in which the progress can be segmented and monitored, which enhances successful project completion. The results from these studies demonstrate how currently available data can be used to provide completely new and meaningful information, when advanced analytics are used. In addition to novel solutions for data analytics, this dissertation research also provided methods for implementing the solutions, as the true benefits of knowledge-based decision making are obtained in only practical railway asset management

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Exploring QCD matter in extreme conditions with Machine Learning

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    In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

    Uncrewed Aerial Vehicle Fruit Picking with Perceptual Imitation Learning Trajectory Generation

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    This thesis studies the problem of Uncrewed Aerial Vehicle (UAV) path planning and manipulation in unmapped environments. This thesis the specific task of orange picking with a quadrotor UAV. Robotic fruit harvesting is a fitting example problem to tackle in this research, as there is a worldwide need for improved agricultural technologies. This task is difficult because it requires comprehending and navigating a complex, unknown environment. To accomplish this task, we present a novel visual servoing controller which fuses information from onboard camera images with odometry data. This was used to calculate the relative position of an orange and a safe approach angle. By following a series of reference trajectories to the computed goal location, the system was able to grasp an orange autonomously and remove it from the tree. This visual servoing method has several inherent limitations. It cannot search for an occluded orange or handle any paths that remove the orange from its view. To improve upon this approach, and correct these shortcomings, we develop a novel neural network architecture to perform the same task using a learned implicit visual encoding. In the next section, we present the design of a simulation of this same orange picking task, and a Model Predictive Control (MPC) method for computing optimal trajectories within it. We trained the neural network to imitate the MPC expert, validating the network structure and cost function. In the subsequent chapter, we trained the same architecture on a dataset derived from the visual servoing controller. These experiments led to useful innovations in the neural network architecture, but even with these efforts, no network was able to vastly improve on the baseline data. In the final chapter, we discuss the relative strengths and weaknesses of these algorithms. Each has areas where it exceeds the others, and we propose new avenues of research to improve them all

    Privaatsust sÀilitava raalnÀgemise meetodi arendamine kehalise aktiivsuse automaatseks jÀlgimiseks koolis

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    VĂ€itekirja elektrooniline versioon ei sisalda publikatsiooneKuidas vaadelda inimesi ilma neid nĂ€gemata? Öeldakse, et ei ole viisakas jĂ”llitada. Õigus privaatsusele on lausa inimĂ”igus. Siiski on inimkĂ€itumises palju sellist, mida teadlased tahaksid uurida inimesi vaadeldes. NĂ€iteks tahame teada, kas lapsed hakkavad vahetunnis rohkem liikuma, kui koolis keelatakse nutitelefonid? Selle vĂ€lja selgitamiseks peaks teadlane kĂŒsima lapsevanematelt nĂ”usolekut vĂ”sukeste vaatlemiseks. Eeldusel, et lapsevanemad annavad loa, oleks klassikaliseks vaatluseks vaja tohutult palju tööjĂ”udu – mitu vaatlejat koolimajas iga pĂ€ev piisavalt pikal perioodil enne ja pĂ€rast nutitelefoni keelu kehtestamist. Doktoritööga pĂŒĂŒdsin lahendada korraga privaatsuse probleemi ja tööjĂ”u probleemi, asendades inimvaatleja tehisaruga. Kaasaegsed masinĂ”ppe meetodid vĂ”imaldavad luua mudeleid, mis tuvastavad automaatselt pildil vĂ”i videos kujutatud objekte ja nende omadusi. Kui tahame tehisaru, mis tunneb pildil Ă€ra inimese, tuleb moodustada masinĂ”ppe andmestik, kus on pilte inimestest ja pilte ilma inimesteta. Kui tahame tehisaru, mis eristaks videos madalat ja kĂ”rget kehalist aktiivsust, on vaja vastavat videoandmestikku. Doktoritöös kogusingi andmestiku, kus video laste liikumisest on sĂŒnkroniseeritud puusal kantavate aktseleromeetritega, et treenida mudel, mis eristaks videopikslites madalamat ja kĂ”rgemat liikumise intensiivsust. Koostöös Tehonoloogiainstituudi iCV laboriga arendasime vĂ€lja videoanalĂŒĂŒsi sensori prototĂŒĂŒbi, mis suudab reaalaja kiirusel hinnata kaamera vaatevĂ€ljas olevate inimeste kehalise aktiivsuse taset. Just see, et tehisaru suudab tuletada videost kehalise aktiivsuse informatsiooni ilma neid videokaadreid salvestamata ega inimestele ĂŒldsegi nĂ€itamata, vĂ”imaldab vaadelda inimesi ilma neid nĂ€gemata. VĂ€ljatöötatud meetod on mĂ”eldud kehalise aktiivsuse mÔÔtmiseks koolipĂ”histes teadusuuringutes ning seetĂ”ttu on arenduses rĂ”hutatud privaatsuse kaitsmist ja teaduseetikat. Laiemalt vaadates illustreerib doktoritöö aga raalnĂ€gemistehnoloogiate potentsiaali töötlemaks visuaalset infot linnaruumis ja töökohtadel ning mitte ainult kehalise aktiivsuse mÔÔtmiseks kĂ”rgete teaduseetika kriteerimitega. Siin ongi koht avalikuks aruteluks – millistel tingimustel vĂ”i kas ĂŒldse on OK, kui sind jĂ”llitab robot?  How to observe people without seeing them? They say it's not polite to stare. The right to privacy is considered a human right. However, there is much in human behavior that scientists would like to study via observation. For example, we want to know whether children will start moving more during recess if smartphones are banned at school? To figure this out, scientists would have to ask parental consent to carry out the observation. Assuming parents grant permission, a huge amount of labour would be needed for classical observation - several observers in the schoolhouse every day for a sufficiently long period before and after the smartphone ban. With my doctoral thesis, I tried to solve both the problem of privacy and of labor by replacing the human observer with artificial intelligence (AI). Modern machine learning methods allow training models that automatically detect objects and their properties in images or video. If we want an AI that recognizes people in images, we need to form a machine learning dataset with pictures of people and pictures without people. If we want an AI that differentiates between low and high physical activity in video, we need a corresponding video dataset. In my doctoral thesis, I collected a dataset where video of children's movement is synchronized with hip-worn accelerometers to train a model that could differentiate between lower and higher levels of physical activity in video. In collaboration with the ICV lab at the Institute of Technology, we developed a prototype video analysis sensor that can estimate the level of physical activity of people in the camera's field of view at real-time speed. The fact that AI can derive information about physical activity from the video without recording the footage or showing it to anyone at all, makes it possible to observe without seeing. The method is designed for measuring physical activity in school-based research and therefore highly prioritizes privacy protection and research ethics. But more broadly, the thesis illustrates the potential of computer vision technologies for processing visual information in urban spaces and workplaces, and not only for measuring physical activity or adhering to high ethical standards. This warrants wider public discussion – under what conditions or whether at all is it OK to have a robot staring at you?https://www.ester.ee/record=b555972
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