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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Smeriglio, Riccardo Rosell-Mirmi, Joana Radeva, Petia Abante, Jordi Universitat de Barcelona Dpt. of Physics Spain Politecnico di Torino Control and Computer Engineering Dpt. Turin Italy Headache Research Group Vall d'Hebron Institut de Recerca Catalonia Barcelona Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Biomedical Sciences Spain Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain

ISBN: (纸本)9798350356632

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases. © 2024 IEEE.

关键词： Prediction models

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OpenMP Offloading to DPU

OpenMP Offloading to DPU

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Muhammad Usman Sergio Iserte Roger Ferrer Antonio J. Peña Dpt. of Computer Science Barcelona Supercomputing Center Spain

Data processing units (DPUs) as network co-processors are gaining momentum in the high-performance computing (HPC) community, offering numerous unexplored opportunities. Typically, DPUs have been utilized as domain-specific accelerators that remain transparent to application developers. In the realm of HPC, DPUs have been employed as MPI accelerators and for offloading certain tasks from general-purpose processors. However, the latter approach required application developers to deploy MPI ranks on DPUs, treating them as remote compute nodes, which significantly impacted code usability.

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When Circular Economy Meets the Smart City Ecosystem: Defining the Smart and Circular City

When Circular Economy Meets the Smart City Ecosystem: Defini...

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IEEE International Smart Cities Conference (ISC2)

作者： Georgios Mylonas Athanasios Kalogeras Sobah Abbas Petersen Luis Muñoz Ioannis Chatzigiannakis Industrial Systems Institute Athena Research Center Patras Greece Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway Dpt. of Communications Engineering Universidad de Cantabria Santander Spain Dpt. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that exceeds current activities that are based on more conventional approaches. We also outline current open challenges in this domain and research questions that still need to be addressed.

关键词： Heart Climate change Smart cities Ecosystems Focusing Fourth Industrial Revolution Proposals Sustainable development Resilience Business

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When Circular Economy Meets the Smart City Ecosystem: Defining the Smart and Circular City

arXiv

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arXiv 2024年

作者： Mylonas, Georgios Kalogeras, Athanasios Petersen, Sobah Abbas Muñoz, Luis Chatzigiannakis, Ioannis Industrial Systems Institute Athena Research Center Patras Greece Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway Dpt. of Communications Engineering Universidad de Cantabria Santander Spain Dpt. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

关键词： Circular economy

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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Guiding Attention in End-to-End Driving Models

Guiding Attention in End-to-End Driving Models

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IEEE Symposium on Intelligent Vehicle

作者： Diego Porres Yi Xiao Gabriel Villalonga Alexandre Levy Antonio M. López Computer Vision Center (CVC) Universitat Autònoma de Barcelona (UAB) Spain Dpt. Ciències de la Computació Universitat Autònoma de Barcelona (UAB) Spain

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. However, training these well-performing models usually requires a huge amount of data, while still lacking explicit and intuitive activation maps to reveal the inner workings of these models while driving. In this paper, we study how to guide the attention of these models to improve their driving quality and obtain more intuitive activation maps by adding a loss term during training using salient semantic maps. In contrast to previous work, our method does not require these salient semantic maps to be available during testing time, as well as removing the need to modify the model’s architecture to which it is applied. We perform tests using perfect and noisy salient semantic maps with encouraging results in both, the latter of which is inspired by possible errors encountered with real data. Using CIL++ as a representative state-of-the-art model and the CARLA simulator with its standard benchmarks, we conduct experiments that show the effectiveness of our method in training better autonomous driving models, especially when data and computational resources are scarce.

关键词： Training Computational modeling Semantics Natural languages computer architecture Benchmark testing Data models

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

引用

IEEE Internet of Things Journal 2025年

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Behavior Change Education for Sustainable Development Energy Literacy Energy Saving Environmental Education Gamification Internet of Things K-12 Sustainability Sustainable Smart Schools

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Active flow control for external aerodynamics: From micro air vehicles to a full aircraft in stall 4

Active flow control for external aerodynamics: From micro ai...

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4th Madrid Summer School on Turbulence

作者： Lehmkuhl, Oriol Lozano-Durán, Adrián Rodriguez, Ivette Dpt. of Computer Applications in Science and Engineering Barcelona Supercomputing Center Spain Center for Turbulence Research Stanford University United States Dpt. of Heat Engines Universitat Politecnica de Catalunya Spain

We investigate the aerodynamic performance of active flow control of airfoils and wings using synthetic jets with zero net-mass flow. The study is conducted via wall-resolved and wall-modeled large-eddy simulation using two independent CFD solvers: Alya, a finite-element-based solver;and charLES, a finite-volume-based solver. Our approach is first validated in a NACA4412, for which numerical and experimental results are already available in the literature. The performance of synthetic jets is evaluated for two flow configurations: a SD7003 airfoil at moderate Reynolds number with laminar separation bubble, which is representative of Micro Air Vehicles, and the high-lift configuration of the JAXA Standard Model at realistic Reynolds numbers for landing. In both cases, our predictions indicate that, at high angles of attack, the control successfully eliminates the laminar/turbulent recirculations located downstream the actuator, which increases the aerodynamic performance. Our efforts illustrate the technology-readiness of large-eddy simulation in the design of control strategies for real-world external aerodynamic applications. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Large eddy simulation

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An augmentation strategy to mimic multi-scanner variability in MRI

arXiv

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arXiv 2021年

作者： Meyer, Maria Ines de la Rosa, Ezequiel Barros, Nuno Paolella, Roberto van Leemput, Koen Sima, Diana M. Health Technology Dpt. Technical University of Denmark Lyngby Denmark icometrix Leuven Belgium Dpt. of Computer Science Technical University of Munich Munich Germany imec Vision Lab University of Antwerp Antwerp Belgium Martinos Center for Biomedical Imaging MGH & Harvard Medical School BostonMA United States

Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data. © 2021, CC BY.

关键词： Scanning

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Symbolic Meta-Modeling of RIS-Assisted FSO Communication Channels

TechRxiv

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TechRxiv 2023年

作者： Ndjiongue, Alain Richard Ngatched, Telex M.N. Dobre, Octavia A. Haas, Harald Shin, Hyundong The Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada The Department of Electrical and Computer Engineering McMaster University Canada The LiFi Research and Development Center Dpt. Electronic and Electrical Engineering The University of Strathclyde Glasgow United Kingdom The School of Electronics and Information Kyung Hee University 1 Seocheon Kihung Kyungki Yongin446-701 Korea Republic of

Due to their advantages and role in the beyond fifth-generation mobile network, free-space optical (FSO) communication systems continue to gain attention. The line-of-sight (LoS) link in FSO communications is fundamental as it delivers all the power detected by the user. Therefore, its obstruction is detrimental to the transmission system. In the event of an obstruction, the LoS requirements can be met by introducing the reconfigurable intelligent surface (RIS) technology. However, with the implementation of an RIS infrastructure, FSO channels are divided into two sub-channels, creating an inherent diversity end-to-end (e2e) channel model. In this paper, leveraging on the Meijer G-functions, which are symbolic representations of mathematical functions, we derive symbolic models for e2e RIS-assisted FSO systems that are built on the probability density functions (PDFs) of the fading model characterizing the sub-channels. We study some of the most popular models, derive closed-form expressions of PDFs, cumulative distributions, and moment-generating functions that are further exploited to analyze systems’ performance. By comparing scenarios in which the sub-channels are similar and different, we provide a clear understanding of how the overall RIS-assisted FSO channel behaves, along with comparative results based on commonly used models. © 2023, CC BY.

关键词： Probability density function

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