5,943 research outputs found

    A sequential sampling-based Bayesian numerical method for reliability-based design optimization

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    For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples

    Meta-learning algorithms and applications

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    Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples. Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number. Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation. More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents

    A Cooperative Resilience-Oriented Planning Framework for Integrated Distribution Energy Systems and Multi-Carrier Energy Microgrids Considering Energy Trading

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    Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.<br/

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

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    Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified

    Contribution of Relatedness and Genetic Factors to the Clinical Picture of Coeliac Disease

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    Keliakia on yleinen autoimmuunisairaus, jossa tärkein altistava ympäristötekijä on ravinnon sisältämä gluteeni. Keliakiaa esiintyy maailmanlaajuisesti, mutta sen esiintyvyys Suomessa on erityisen korkea, vaikkakin laajalti alidiagnosoitu. Osasyy tälle on keliakian kirjava taudinkuva. Ruuansulatuskanavan oireet muodostivat pitkään keliakialle tyypillisen oirekuvan, mutta se on vähitellen korvautunut lievemmillä, usein ruuansulatuskanavan ulkopuolisilla oireilla. Nykyään keliakia voi ilmentyä myös täysin oireettomana. Oireiden lisäksi niiden puhkeamisessa, voimakkuudessa sekä kehittymisessä on paljon yksilöllisiä eroja. Myös keliakiapotilaiden vaste gluteenittomaan ruokavaliohoitoon vaihtelee. Syitä tai tekijöitä tämän potilaskohtaisesti vaihtelevan taudinkuvan taustalla ei kuitenkaan vielä tunneta. Nykyisin moni keliaakikko on osannut aiemmin epäillä kohdallaan keliakiaa ja hakeutunut lääkäriin, koska sairaus on ollut tuttu keliakiaa sairastavien sukulaisten tai perheenjäsenten kautta. Nämä ns. familiaaliset keliaakikot ovat enemmistö, mutta keliakiaa esiintyy myös ns. sporadisesti, jolloin potilaalla ei ole tiedettyä sukutaustaa keliakialle. Tunnetuin keliakialle altistava perintötekijä on HLA-DQ2.5-haplotyyppi, mutta muidenkin, HLA-alueen ulkopuolisten, geenialueiden on todettu assosioituvan keliakiaan. Tässä väitöskirjatutkimuksessa selvitettiin familiaalisen ja sporadisen keliakian eroavaisuuksia perintötekijöiden ja taudinkuvan suhteen. Lisäksi tutkittiin, onko HLA-DQ2.5-kuormalla (eli sillä, onko potilas HLA-DQ2.5-negatiivinen, - heterotsygootti vai -homotsygootti) ja HLA:n ulkopuolisilla geneettisillä varianteilla, mukaan lukien variantit kandidaattigeeneissä CCR9 ja CCL25, vaikutusta keliakian kliiniseen taudinkuvaan. Tämän väitöskirjan tulokset paljastivat, että sporadinen keliakia omana erillisenä tautimuotonaan eroaa HLA-DQ-genotyypiltään familiaalisesta tautimuodosta, vaikka useita eroavaisuuksia kliinisen taudinkuvan suhteen ei löydettykään. Sporadisten potilaiden taudinkuva diagnoosihetkellä oli kuitenkin vakavampi ja yleinen terveydentila seurantahetkellä, gluteenittomalla dieetillä, huonompi kuin familiaalisilla potilailla. Tutkimukset myös osoittivat, että HLA-DQ2.5-negatiivisia potilaita leimasi HLA-DQ2.5-heterotsygootteja ja -homotsygootteja useammin klassinen oirekuva diagnoosihetkellä sekä pitkittyneet oireet seurantahetkellä. Nämä tutkimuslöydökset toivottavasti rohkaisevat lääkäreitä sekä muuta hoitohenkilökuntaa kiinnittämään erityistä huomiota edellä mainittuihin potilasryhmiin. Lisäksi väitöstutkimuksen geneettisten analyysien tulokset osoittivat neljän HLA-alueen ulkopuolisen variantin assosioituvan familiaaliseen keliakiaan. Näiden assosiaatioiden merkitys tulee kuitenkin vahvistaa jatkotutkimuksilla. HLA- DQ2.5-kuorman tai CCR9- ja CCL25-kandidaattigeenialueiden varianttien vaikutus keliakian taudinkuvaan oli tämän väitöskirjatutkimuksen perusteella vähäinen.Coeliac disease, a systemic autoimmune disorder induced by dietary gluten, is widespread globally, but in Finland even particularly prevalent although still heavily underdiagnosed. One reason contributing to this is the remarkably multifaceted clinical picture of coeliac disease. The originally typical gastrointestinal symptoms are currently being increasingly replaced by a milder presentation with extraintestinal symptoms and even totally asymptomatic presentation is no longer abnormal. Nevertheless, coeliac disease patients do not differ from each other only in terms of symptoms. There is a wide individual variation concerning the onset, severity and progression of symptoms as well as response to dietary treatment i.e., gluten-free diet (GFD). The reasons and contributary factors underlying this wide clinical heterogeneity remain obscure. For many people nowadays, suspicion of coeliac disease is closely connected to known familial history of the disease. These familial cases are in the majority, but not every patient has affected relatives and such patients are considered sporadic. Where coeliac disease heredity is concerned, HLA-DQ2.5 is the best-known genetic component. However, numerous loci outside the HLA region have also been associated with the disease. The studies presented in this dissertation focused in investigating whether there are genetic and/or phenotypic differences between familial and sporadic coeliac disease and whether the dose of HLA-DQ2.5 or the presence of genetic variants outside HLA region, including the ones within candidate genes CCR9 and CCL25, contributes to the clinical picture of the disease. The findings of this dissertation managed to describe sporadic coeliac disease as an independent entity with a distinct HLA-DQ genotype even though not many significant phenotypic differences were observed between familial and sporadic coeliac disease. Nevertheless, the sporadic cases had more severe clinical phenotype at diagnosis as well as poorer overall health even after dietary treatment. Moreover, HLA-DQ2.5-negative coeliac disease patients were observed to present with classical phenotype at diagnosis as well as with persistent symptoms after dietary treatment more often than patients heterozygous or homozygous for high-risk HLA- DQ2.5. These findings will hopefully encourage physicians to pay special attention to both these patient groups. Four distinct non-HLA variants were associated with increased risk for familial coeliac disease, but the associations need to be confirmed in future studies. The contribution of HLA-DQ2.5 dose as well as non-HLA single nucleotide polymorphisms (SNPs) within CCR9 and CCL25 to the clinical picture of coeliac disease was found to be only modest

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum
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