419 results on '"Aparicio, Alberto"'
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2. Accept no limits: biocontainment and the construction of a safer space for experimentation in xenobiology as a legacy of Asilomar
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Aparicio, Alberto
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- 2024
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3. Engaging with a Xenobiology Laboratory as a Social Scientist: Lessons, Opportunities, and Challenges
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Aparicio, Alberto and Greco, Cinzia, editor
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- 2024
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4. Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
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Bruschi, Nazareno, Tagliavini, Giuseppe, Conti, Francesco, Abadal, Sergi, Cabellos-Aparicio, Alberto, Alarcón, Eduard, Karunaratne, Geethan, Boybat, Irem, Benini, Luca, and Rossi, Davide
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Computer Science - Hardware Architecture ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm for heterogeneous computing, potentially delivering orders of magnitude better peak performance and efficiency over traditional digital signal processing architectures on Matrix-Vector multiplication. However, to sustain this throughput in real-world applications, AIMC tiles must be supplied with data at very high bandwidth and low latency; this poses an unprecedented pressure on the on-chip communication infrastructure, which becomes the system's performance and efficiency bottleneck. In this context, the performance and plasticity of emerging on-chip wireless communication paradigms provide the required breakthrough to up-scale on-chip communication in large AIMC devices. This work presents a many-tile AIMC architecture with inter-tile wireless communication that integrates multiple heterogeneous computing clusters, embedding a mix of parallel RISC-V cores and AIMC tiles. We perform an extensive design space exploration of the proposed architecture and discuss the benefits of exploiting emerging on-chip communication technologies such as wireless transceivers in the millimeter-wave and terahertz bands.
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- 2022
5. Are ready biodegradation tests effective screens for non-persistence in all environmental compartments?
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Martin-Aparicio, Alberto, Camenzuli, Louise, Hughes, Christopher, Pemberton, Emma, Saunders, David, Wang, Neil, and Lyon, Delina Y.
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- 2023
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6. Policy landscapes on human genome editing: a perspective from Latin America
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Saldaña-Tejeda, Abril, Aparicio, Alberto, González-Santos, Sandra P., Arguedas-Ramírez, Gabriela, Cavalcanti, Juliana Manzoni, Shaw, Malissa Kay, and Perler, Laura
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- 2022
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7. EL DELITO DE INCUMPLIMIENTO DE LAS SANCIONES INTERNACIONALES Y LA DIRECTIVA (UE) 2024/1226, DE 24 DE ABRIL.
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Vieira da Costa, Patricia Leandro and Schoch Aparicio, Alberto
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INTERNATIONAL sanctions , *CRIMINAL law - Abstract
Directive (EU) 2024/1226 of 24 April imposes on Member States the obligation to criminalise violations of EU international sanctions (or restrictive measures). This article briefly analyses how this directive will affect Spanish criminal law. [ABSTRACT FROM AUTHOR]
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- 2024
8. Mobile Intensive Care Unit versus Hospital walk-in patients, in the treatment of first episode ST- elevation myocardial infarction
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Viejo-Moreno, Rubén, Cabrejas-Aparicio, Alberto, Arriero-Fernández, Noemí, Quintana-Díaz, Manuel, Galván-Roncero, Enrique, Gálvez-Marco, María de las Nieves, Carriedo-Scher, Cristina, Balaguer-Recena, Javier, and Marian-Crespo, Carlos
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- 2020
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9. Multi-agent graph learning-based optimization and its applications to computer networks
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Bernárdez Gil, Guillermo, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barlet Ros, Pere, Cabellos Aparicio, Alberto, and Bernárdez Gil, Guillermo
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Tesi amb menció de Doctorat Internacional, (English) In the wake of a digital revolution, contemporary society finds itself entrenched in an era where network applications' demands surpass the capabilities of conventional network management solutions. This dissertation navigates through the intricacies of modern networked environments, where traditional management approaches are falling short due to emerging applications like augmented and virtual reality, holographic telepresence, and vehicular networks, demanding ultra-low latency and robust adaptability. These evolving networks form the backbone of modern society, sustaining numerous vital services but posing elevated complexity and operational hurdles for Internet Service Providers (ISPs) and network operators. Amidst this complexity, the need for innovative solutions to optimize and manage today's networks is more pronounced than ever. A central proposition of this dissertation is the MAGNNETO framework, a groundbreaking Machine Learning (ML) based initiative that stands for Multi-Agent Graph Neural Network Optimization. This framework is at the heart of the endeavour to facilitate distributed optimization in networked scenarios. By integrating a Graph Neural Network (GNN) architecture into a Multi-Agent Reinforcement Learning (MARL) setting, it instigates a fully distributed optimization process and capitalizes on the inherent distributed nature of networked environments, hence potentially addressing scalability issues and facilitating real-time applications. This initiative is adaptable, offering versatility in addressing various use cases and showcasing robustness to meet the challenging requisites of real-world applications. A substantial contribution of this work is the successful implementation of MAGNNETO across different relevant networked cases, prominently focusing on two highly impactful scenarios within the computer network field. Initially, it re-examines the pivotal issue of Traffic Engineering (TE) optimization in ISP networks. With the g, (Español) En el contexto de una revolución digital, la sociedad contemporánea se encuentra inmersa en una era donde las demandas de las aplicaciones en red superan las capacidades de las soluciones de gestión convencionales. Precisamente, esta tesis navega a través de las complejidades de los entornos de redes modernas, donde los enfoques de gestión tradicionales se están quedando cortos debido a aplicaciones emergentes como la realidad aumentada, la realidad virtual o la telepresencia holográfica, las cuales exigen ultra baja latencia y adaptabilidad dinámica. Estas redes en evolución conforman la columna vertebral de la sociedad moderna, manteniendo numerosos servicios vitales pero planteando una complejidad elevada para los Proveedores de Servicios de Internet (ISP) y los operadores de red. En medio de esta complejidad, la necesidad de soluciones innovadoras para optimizar y gestionar las redes actuales es más evidente que nunca. Una proposición central de esta tesis es el marco MAGNNETO (Optimización Multiagente con Redes Neuronales Gráficas, en sus siglas en inglés), una iniciativa revolucionaria basada en Aprendizaje Automático (ML). Su objetivo principal es facilitar la optimización distribuida en escenarios de redes. Al integrar una arquitectura de Redes Neuronales Gráficas (GNN) en un entorno de Aprendizaje por Refuerzo Multiagente (MARL), da pie a un proceso de optimización completamente distribuido y aprovecha la naturaleza distribuida inherente de los entornos de red, abordando problemas de escalabilidad y facilitando aplicaciones en tiempo real. Esta iniciativa es adaptable, ofreciendo versatilidad para abordar varios casos de uso y mostrando robustez para cumplir con los desafiantes requisitos de aplicaciones reales. Una contribución sustancial de este trabajo es la implementación exitosa de MAGNNETO en diferentes casos relevantes de redes, centrándose prominentemente en dos escenarios altamente impactantes en el campo de las redes de computadores. En, Postprint (published version)
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- 2024
10. Alberto Aparicio
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Aparicio, Alberto, primary
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- 2024
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11. For Analytics Beyond “Personhood,” Bioethics Should Look Toward Science and Technology Studies (STS)
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Molldrem, Stephen, primary, Moses, Jacob D., additional, Aparicio, Alberto, additional, and Subrahmanyam, Vishnu, additional
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- 2024
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12. Life Extension Should Come with Wisdom: Reflections and Questions for the Geroscience and Longevity Community.
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Aparicio, Alberto
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PUBLIC opinion , *SOCIAL impact , *INVESTORS , *LONGEVITY , *NEW business enterprises - Abstract
Geroscience, or longevity biotechnology, has made impressive advances in recent years that have led to the founding of dozens of start-ups, nonprofits and advocacy organizations, and the formation of a global movement to defeat aging. The community envisions changes at the regulatory and policy levels and calls for increased funding for research. Nevertheless, progress in the field has not been matched by discussions about ethical, legal, and social implications, as longevity advocates assume that seeking to expand lifespan or health span is inherently desirable and permissible. In this article, I make the case for the importance of putting ethics and society back into geroscience, along with three considerations for the longevity community. First, it should seek to understand the needs and attitudes of the public. Second, the community needs to define whether the field is primarily striving for healthy aging (increasing health span) or for extending years of life (lifespan). Third, it needs to define the role of investors and tech millionaires in shaping the field's priorities and direction. This last point raises the question of who is setting the direction of a field that can reshape the meaning of being human. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Comprehensive Two-Dimensional Gas Chromatography with Peak Tracking for Screening of Constituent Biodegradation in Petroleum UVCB Substances
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Booth, Andy M., primary, Sørensen, Lisbet, additional, Brakstad, Odd G., additional, Ribicic, Deni, additional, Creese, Mari, additional, Arey, J. Samuel, additional, Lyon, Delina Y., additional, Redman, Aaron D., additional, Martin-Aparicio, Alberto, additional, Camenzuli, Louise, additional, Wang, Neil, additional, and Gros, Jonas, additional
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- 2023
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14. Unveiling the potential of Graph Neural Networks for robust Intrusion Detection
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Pujol Perich, David, Suárez-Varela Maciá, José Rafael|||0000-0002-7141-3414, Cabellos Aparicio, Alberto|||0000-0001-9329-7584, Barlet Ros, Pere|||0000-0001-7837-0886, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, and Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
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Networking and Internet Architecture (cs.NI) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Ordinadors, Xarxes d' -- Mesures de seguretat ,Computer Science - Cryptography and Security ,Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC] ,Cybersecurity ,Computer Science - Artificial Intelligence ,Computer Networks and Communications ,Seguretat informàtica ,Graph neural networks ,Machine Learning (cs.LG) ,Computer Science - Networking and Internet Architecture ,Neural networks (Computer science) ,Informàtica::Seguretat informàtica [Àrees temàtiques de la UPC] ,Artificial Intelligence (cs.AI) ,Computer security ,Computer networks -- Security measures ,Hardware and Architecture ,Machine learning ,Aprenentatge automàtic ,Xarxes neuronals (Informàtica) ,Network intrusion detection ,Cryptography and Security (cs.CR) ,Software - Abstract
The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works propose the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS are barely robust to common adversarial attacks, which limits their applicability to real networks. A fundamental problem of these solutions is that they treat and classify flows independently. In contrast, in this paper we argue the importance of focusing on the structural patterns of attacks, by capturing not only the individual flow features, but also the relations between different flows (e.g., the source/destination hosts they share). To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information. In our evaluation, we first show that the proposed GNN model achieves state-of-the-art results in the well-known CIC-IDS2017 dataset. Moreover, we assess the robustness of our solution under two common adversarial attacks, that intentionally modify the packet size and inter-arrival times to avoid detection. The results show that our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under these attacks. This unprecedented level of robustness is mainly induced by the capability of our GNN model to learn flow patterns of attacks structured as graphs., Comment: 7 pages, 4 figures, 1 table
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- 2022
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15. Performance-oriented digital twins for packet and optical networks
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Cabellos Aparicio, Alberto, Janz, Christopher, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Barlet Ros, Pere, Paillissé Vilanova, Jordi, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Guo, Aihua, Perino, Diego, Lopez, Diego, Pastor Perales, Antonio Agustín, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Cabellos Aparicio, Alberto, Janz, Christopher, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Barlet Ros, Pere, Paillissé Vilanova, Jordi, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Guo, Aihua, Perino, Diego, Lopez, Diego, and Pastor Perales, Antonio Agustín
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This draft introduces the concept of a Network Digital Twin (NDT), including the architecture as well as the interfaces. Then two specific instances of the NDT are introduced, the first one for packet networks. This produces performance estimates (delay, jitter, loss) for a packet network with a specified topology, traffic demand, and routing and scheduling configuration. Second, a NDT for optical networks, this produces transmission performance estimates of an optical network with specified optical service topologies and network equipment types, topology and status., Preprint
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- 2023
16. Implementing spatio-temporal GNN in IGNNITION
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Paillissé Vilanova, Jordi, Ferriol Galmés, Miquel, Cabellos Aparicio, Alberto, Torres i Garzo, Carles, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Paillissé Vilanova, Jordi, Ferriol Galmés, Miquel, Cabellos Aparicio, Alberto, and Torres i Garzo, Carles
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GNNITIOn is a powerful library that allows the user to generate quick GNNs without needing to learn to use the deep learning libraries. STGNNs (Spati-Temporal GNNs) are a variation of the GNN that takes into account the variability of the data through the time. The goal of this master thesis is to learn how the STGNNs work by developing one from literature and give IGNNITION the tools to be able to develop STGNNs.
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- 2023
17. El trabajo con los animales de la granja en el aula de educación infantil
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Bajo Aparicio, Alberto, Román Grande, María Teresa, Universidad de Valladolid. Facultad de Educación de Palencia, Bajo Aparicio, Alberto, Román Grande, María Teresa, and Universidad de Valladolid. Facultad de Educación de Palencia
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El TFG en educación infantil que se presenta a continuación tiene como objetivo explorar y analizar el papel del trabajo con los animales de la granja en el desarrollo educativo de los niños en el ámbito de la educación infantil. Se examina la importancia de esta experiencia educativa en relación con el aprendizaje de los niños, su conexión con la naturaleza y el fomento de habilidades socioemocionales. Este TFG también aborda la importancia de la planificación y la integración de estas actividades en el currículo de educación infantil, así como la necesidad de proporcionar un entorno seguro y adecuado para los niños y los animales. Se lleva a cabo a su vez un análisis de las implicaciones prácticas y las recomendaciones para los educadores que deseen implementar actividades con animales de granja en sus aulas. Todo lo comentado anteriormente se encuentra encuadrado en un marco teórico contrastado y detallado que dota de sentido y rigor a la propuesta de intervención construida., Grado en Educación Infantil
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- 2023
18. Graph neural networks for communication networks: context, use cases and opportunities
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Suárez-Varela Maciá, José Rafael, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Rusek, Krzysztof, Geyer, Fabien, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Scarselli, Franco, Cabellos Aparicio, Alberto, Barlet Ros, Pere, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Suárez-Varela Maciá, José Rafael, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Rusek, Krzysztof, Geyer, Fabien, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Scarselli, Franco, Cabellos Aparicio, Alberto, and Barlet Ros, Pere
- Abstract
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area., This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
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- 2023
19. RouteNet-Fermi: network modeling with graph neural networks
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Ferriol Galmés, Miquel, Paillissé Vilanova, Jordi, Suárez-Varela Maciá, José Rafael, Rusek, Krzysztof, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Ferriol Galmés, Miquel, Paillissé Vilanova, Jordi, Suárez-Varela Maciá, José Rafael, Rusek, Krzysztof, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles — e.g., with complex non-Markovian models — and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network., This work was supported in part by the Spanish I+D+i Project TRAINER-A funded by the Ministry of Science and Innovation (MCIN)/State Investigation Agency (AEI)/10.13039/501100011033 under Grant PID2020-118011GB-C21, in part by the Catalan Institution for Research and Advanced Studies (ICREA), and in part by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
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- 2023
20. Bayesian inference of spatial and temporal relations in AI patents for EU countries
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Kleszcz, Agnieszka, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Kleszcz, Agnieszka, and Cabellos Aparicio, Alberto
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In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction between countries or explain the rapidly growing trends in AI patents. For spatial analysis Poisson regression is used to explain collaboration between a pair of countries measured by the number of common patents. Through Bayesian inference, we estimated the strengths of interactions between countries in the EU and the rest of the world. In particular, a significant lack of cooperation has been identified for some pairs of countries. Alternatively, an inhomogeneous Poisson process combined with the logistic curve growth accurately models the temporal behaviour by an accurate trend line. Bayesian analysis in the time domain revealed an upcoming slowdown in patenting intensity., The research was supported in part by PL-Grid Infrastructure, POWER 2014–2020 program and the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University., Peer Reviewed, Postprint (published version)
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- 2023
21. MAGNNETO: A graph neural network-based multi-agent system for traffic engineering
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bernárdez Gil, Guillermo, Suárez-Varela Maciá, José Rafael, López Brescó, Albert, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bernárdez Gil, Guillermo, Suárez-Varela Maciá, José Rafael, López Brescó, Albert, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in Internet Service Provider (ISP) networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as Local search, Constraint Programming, or Linear programming. In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization. MAGNNETO deploys a set of agents across the network that learn and communicate in a distributed fashion via message exchanges between neighboring agents. Particularly, we apply this framework to optimize link weights in Open Shortest Path First (OSPF), with the goal of minimizing network congestion. In our evaluation, we compare MAGNNETO against several state-of-the-art TE optimizers in more than 75 topologies (up to 153 nodes and 354 links), including realistic traffic loads. Our experimental results show that, thanks to its distributed nature, MAGNNETO achieves comparable performance to state-of-the-art TE optimizers with significantly lower execution times. Moreover, our ML-based solution demonstrates a strong generalization capability to successfully operate in new networks unseen during training., This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GBC21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
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- 2023
22. Multi-channel medium access control protocols for wireless networks within computing packages
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Ollé Ferrayuoli, Bernat, Talarn Bell-lloch, Pau, Cabellos Aparicio, Alberto, Lemic, Filip, Alarcón Cot, Eduardo José, Abadal Cavallé, Sergi, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Ollé Ferrayuoli, Bernat, Talarn Bell-lloch, Pau, Cabellos Aparicio, Alberto, Lemic, Filip, Alarcón Cot, Eduardo José, and Abadal Cavallé, Sergi
- Abstract
Wireless communications at the chip scale emerge as a interesting complement to traditional wire-based approaches thanks to their low latency, inherent broadcast nature, and capacity to bypass pin constraints. However, as current trends push towards massive and bandwidth-hungry processor architectures, there is a need for wireless chip-scale networks that exploit and share as many channels as possible. In this context, this work addresses the issue of channel sharing by exploring the design space of multi-channel Medium Access Control (MAC) protocols for chip-scale networks. Distinct channel assignment strategies for both random access and token passing are presented and evaluated under realistic traffic patterns. It is shown that, even with the improvements enabled by the multiple channels, both protocols maintain their intrinsic advantages and disadvantages., This work is supported by the European Commission under H2020 grant WiPLASH (GA 863337) and HE grant WINC (GA 101042080)., Peer Reviewed, Postprint (author's final draft)
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- 2023
23. Comprehensive Two-Dimensional Gas Chromatography with Peak Tracking for Screening of Constituent Biodegradation in Petroleum UVCB Substances
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Booth, Andy M., Sørensen, Lisbet, Brakstad, Odd G., Ribicic, Deni, Creese, Mari, Arey, J. Samuel, Lyon, Delina Y., Redman, Aaron D., Martin-Aparicio, Alberto, Camenzuli, Louise, Wang, Neil, Gros, Jonas, Booth, Andy M., Sørensen, Lisbet, Brakstad, Odd G., Ribicic, Deni, Creese, Mari, Arey, J. Samuel, Lyon, Delina Y., Redman, Aaron D., Martin-Aparicio, Alberto, Camenzuli, Louise, Wang, Neil, and Gros, Jonas
- Abstract
Petroleum substances, as archetypical UVCBs (substances of unknown or variable composition, complex reaction products, or biological substances), pose a challenge for chemical risk assessment as they contain hundreds to thousands of individual constituents. It is particularly challenging to determine the biodegradability of petroleum substances since each constituent behaves differently. Testing the whole substance provides an average biodegradation, but it would be effectively impossible to obtain all constituents and test them individually. To overcome this challenge, comprehensive two-dimensional gas chromatography (GC × GC) in combination with advanced data-handling algorithms was applied to track and calculate degradation half-times (DT50s) of individual constituents in two dispersed middle distillate gas oils in seawater. By tracking >1000 peaks (representing ∼53–54% of the total mass across the entire chromatographic area), known biodegradation patterns of oil constituents were confirmed and extended to include many hundreds not currently investigated by traditional one-dimensional GC methods. Approximately 95% of the total tracked peak mass biodegraded after 64 days. By tracking the microbial community evolution, a correlation between the presence of functional microbial communities and the observed progression of DT50s between chemical classes was demonstrated. This approach could be used to screen the persistence of GC × GC-amenable constituents of petroleum substance UVCBs.
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- 2023
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24. Leveraging graph neural networks for optimization and traffic compression in network digital twins
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Cabellos Aparicio, Alberto, Barlet Ros, Pere, Almasan Puscas, Felician Paul, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Cabellos Aparicio, Alberto, Barlet Ros, Pere, and Almasan Puscas, Felician Paul
- Abstract
Tesi amb menció de Doctorat Internacional, (English) In recent years, several industry sectors have adapted the Digital Twin (DT) paradigm to improve the performance of physical systems. This paradigm consists of leveraging computational methods to build high-fidelity virtual representations of a physical system or entity. The virtual replica accurately simulates or models the behavior of the physical system without altering its behavior in the real world. Since its inception, the DT has attracted the interest of both academia and industry which can be observed by the growing number of publications, processes, standards and concepts. The networking community has adapted the DT paradigm with the objective of achieving efficient control and management in modern communication networks. In this context, the Network Digital Twin (NDT) is a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models. NDTs can be applied to many fundamental networking applications. For example, the NDT allows network operators to design novel network optimization solutions, to perform troubleshooting, what-if analysis, or to plan network upgrades taking into account the network’s expected user growth. Since the interaction between the network operator with the NDT does not require access to the real-world network, the aforementioned processes can be carried out in real-time, without jeopardizing the physical network. This dissertation aims to develop new efficient real-time optimization mechanisms leveraging NDTs. Existing network optimization techniques can be generally divided among optimizer-based solutions (e.g., CP, ILP), heuristics and Machine Learning-based (ML) solutions. Optimizer-based solutions are computationally intensive and they suffer from scalability issues where the optimization time and the problem instance size scale at different speeds. The methods based on heuristics are solutions designed by human experts, making strong assumptions and simplifications on th, (Català) En els últims anys, diversos sectors industrials han adaptat el paradigma del "Digital Twin" (DT) per millorar el rendiment dels sistemes físics. Aquest paradigma consisteix en aprofitar mètodes computacionals per construir representacions virtuals d'alta fidelitat d'un sistema o entitat física. La rèplica virtual simula o modela amb precisió el comportament del sistema físic sense alterar el seu comportament en el món real. Des de la seva creació, el DT ha suscitat l'interès tant de l'acadèmia com de la indústria, el que es pot observar pel creixent nombre de publicacions, processos, estàndards i conceptes. La comunitat de xarxes ha adaptat el paradigma del DT amb l'objectiu d'aconseguir un control i una gestió eficients en les xarxes de comunicació modernes. En aquest context, el "Network Digital Twin" (NDT) és un concepte renovat de les eines de modelatge de xarxes clàssiques que té com a objectiu construir models de xarxes precisos basats en dades. Els NDT es poden aplicar a moltes aplicacions fonamentals de xarxes. Per exemple, els NDT permeten als operadors de xarxes dissenyar noves solucions d'optimització de xarxes, realitzar resolució de problemes, anàlisi de supòsits o planificar actualitzacions de xarxa tenint en compte el creixement esperat dels usuaris de la xarxa. A més, els processos esmentats es poden dur a terme en temps real sense posar en perill la xarxa física. Aquesta dissertació té com a objectiu desenvolupar nous mecanismes d'optimització en temps real eficients aprofitant els NDTs. Les tècniques d'optimització de xarxa existents es poden dividir en solucions basades en optimitzadors, solucions basades en heurístiques i solucions basades en aprenentatge automàtic (ML). Les solucions basades en optimitzadors són intensives computacionalment i pateixen problemes d'escalabilitat, on el temps d'optimització i la mida de la instància del problema escalen a diferents velocitats. Els mètodes basats en heurístiques són solucions dissenyades p, Postprint (published version)
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- 2023
25. Deep learning for wireless communications
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Northeastern University, Cabellos Aparicio, Alberto, Chowdhury, Kaushik, Sirera Perelló, Miquel, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Northeastern University, Cabellos Aparicio, Alberto, Chowdhury, Kaushik, and Sirera Perelló, Miquel
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- 2023
26. RiskNet: neural risk assessment in networks of unreliable resources
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Borylo, Piotr, Jaglarz, Piotr, Geyer, Fabien, Cabellos Aparicio, Alberto, Cholda, Piotr, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Borylo, Piotr, Jaglarz, Piotr, Geyer, Fabien, Cabellos Aparicio, Alberto, and Cholda, Piotr
- Abstract
We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations., This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology (P.B., P.C.) and by the PL-Grid Infrastructure (K.R.)., Peer Reviewed, Postprint (published version)
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- 2023
27. Are Ready Biodegradation Tests Effective Screens for Non-persistence in All Environmental Compartments?
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Martin-Aparicio, Alberto, primary, Camenzuli, Louise, additional, Hughes, Christopher, additional, Pemberton, Emma, additional, Saunders, David, additional, Wang, Neil, additional, and Lyon, Delina Y., additional
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- 2023
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28. Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances
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Arey, J. Samuel, primary, Martin Aparicio, Alberto, additional, Vaiopoulou, Eleni, additional, Forbes, Stuart, additional, and Lyon, Delina, additional
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- 2022
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29. Missing the "We" in Precision Medicine.
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Aparicio, Alberto
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DIVERSITY & inclusion policies , *HEALTH policy , *INDIVIDUALIZED medicine , *PUBLIC health , *GENOMICS , *HEALTH promotion - Abstract
The article focuses on Ilaria Galasso's critique of precision medicine initiatives like the US' Precision Medicine Initiative and the British 100,000 Genomes Project, highlighting their failure to address barriers to participation and equitable distribution of benefits, which may exacerbate health inequalities. It Galasso argues that while precision medicine aims to revolutionize healthcare, it alone cannot rectify health disparities.
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- 2024
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30. Results in the Surgical Treatment of Giant Acoustic Neuromas
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Giordano, Ana Inés, Domènech, Ivan, Torres, Alberto, Skufca, Javier, Callejo, Angela, Palomino, Laura, Aparicio, Alberto, Junyent, Josefina, and Mañós, Manuel
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- 2012
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31. Resultados en el tratamiento quirúrgico de los neurinomas del acústico gigantes
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Giordano, Ana Inés, Domènech, Ivan, Torres, Alberto, Skufca, Javier, Callejo, Angela, Palomino, Laura, Aparicio, Alberto, Junyent, Josefina, and Mañós, Manuel
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- 2012
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32. The road ahead: narratives and imaginaries of the value of biodiversity in shaping bioeconomy policy in Colombia
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Aparicio, Alberto, primary
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- 2022
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33. Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
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Bruschi, Nazareno, primary, Tagliavini, Giuseppe, additional, Conti, Francesco, additional, Abadal, Sergi, additional, Cabellos-Aparicio, Alberto, additional, Alarcon, Eduard, additional, Karunaratne, Geethan, additional, Boybat, Irem, additional, Benini, Luca, additional, and Rossi, Davide, additional
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- 2022
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34. El uso de materiales reales en el aula de inglés para la integración: análisis y propuesta de intervención
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Bajo Aparicio, Alberto, Pérez Fernández, Tamara, Universidad de Valladolid. Facultad de Educación de Palencia, Bajo Aparicio, Alberto, Pérez Fernández, Tamara, and Universidad de Valladolid. Facultad de Educación de Palencia
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El TFG que se presenta a continuación tiene como objetivo principal la creación de una propuesta de intervención educativa para un aula de 4º curso de educación primaria centrada en el uso de los materiales reales para la enseñanza de la primera lengua extranjera (inglés). El aula mencionada cuenta con un gran número de adaptaciones curriculares tanto significativas como no significativas y varios casos de escolarización tardía y absentismo escolar, es por ello por lo que a través de la mencionada propuesta se pretende crear un hilo conductor que permita trabajar a toda la clase en una misma dirección y a un ritmo similar, teniendo en cuenta las circunstancias de la clase y los ritmos de aprendizaje. Previo a la construcción de la construcción de la propuesta se ha llevado a cabo un proceso de búsqueda y análisis de referencias bibliográficas que fundamenten la propuesta y todo lo que se va a incluir en la misma., Grado en Educación Primaria
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- 2022
35. Scale up your In-Memory Accelerator: leveraging wireless-on-chip communication for AIMC-based CNN inference
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bruschi, Nazareno, Tagliavini, Giuseppe, Conti, Francesco, Abadal Cavallé, Sergi, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, Karunaratne, Geethan, Boybat, Irem, Benini, Luca, Rossi, Davide, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Bruschi, Nazareno, Tagliavini, Giuseppe, Conti, Francesco, Abadal Cavallé, Sergi, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, Karunaratne, Geethan, Boybat, Irem, Benini, Luca, and Rossi, Davide
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Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm for heterogeneous computing, potentially delivering orders of magnitude better peak performance and efficiency over traditional digital signal processing architectures on Matrix-Vector multiplication. However, to sustain this throughput in real-world applications, AIMC tiles must be supplied with data at very high bandwidth and low latency; this poses an unprecedented pressure on the on-chip communication infrastructure, which becomes the system's performance and efficiency bottleneck. In this context, the performance and plasticity of emerging on-chip wireless communication paradigms provide the required breakthrough to up-scale on-chip communication in large AIMC devices. This work presents a many-tile AIMC architecture with inter-tile wireless communication that integrates multiple heterogeneous computing clusters, embedding a mix of parallel RISC-V cores and AIMC tiles. We perform an extensive design space exploration of the proposed architecture and discuss the benefits of exploiting emerging on-chip communication technologies such as wireless transceivers in the millimeter-wave and terahertz bands., This work was supported by the WiPLASH project (g.a. 863337), founded from the European Union’s Horizon 2020 research and innovation program., Peer Reviewed, Postprint (author's final draft)
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- 2022
36. Network digital twin: context, enabling technologies, and opportunities
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Paillissé Vilanova, Jordi, Suárez-Varela Maciá, José Rafael, Perino, Diego, Lopez, Diego, Pastor Perales, Antonio Agustín, Harvey, Paul, Ciavaglia, Laurent, Wong, Leon, Xiao, Shihan, Ram, Vishnu, Shi, Xiang, Cheng, Xiangle, Cabellos Aparicio, Alberto, Barlet Ros, Pere, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Almasan Puscas, Felician Paul, Ferriol Galmés, Miquel, Paillissé Vilanova, Jordi, Suárez-Varela Maciá, José Rafael, Perino, Diego, Lopez, Diego, Pastor Perales, Antonio Agustín, Harvey, Paul, Ciavaglia, Laurent, Wong, Leon, Xiao, Shihan, Ram, Vishnu, Shi, Xiang, Cheng, Xiangle, Cabellos Aparicio, Alberto, and Barlet Ros, Pere
- Abstract
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real-time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then, we present a case study that leverages a ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020- 118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
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- 2022
37. Building a Digital Twin for network optimization using graph neural networks
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Ferriol Galmés, Miquel, Suárez-Varela Maciá, José Rafael, Paillissé Vilanova, Jordi, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Ferriol Galmés, Miquel, Suárez-Varela Maciá, José Rafael, Paillissé Vilanova, Jordi, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Network modeling is a critical component of Quality of Service (QoS) optimization. Current networks implement Service Level Agreements (SLA) by careful configuration of both routing and queue scheduling policies. However, existing modeling techniques are not able to produce accurate estimates of relevant SLA metrics, such as delay or jitter, in networks with complex QoS-aware queueing policies (e.g., strict priority, Weighted Fair Queueing, Deficit Round Robin). Recently, Graph Neural Networks (GNNs) have become a powerful tool to model networks since they are specifically designed to work with graph-structured data. In this paper, we propose a GNN-based network model able to understand the complex relationship between the queueing policy (scheduling algorithm and queue sizes), the network topology, the routing configuration, and the input traffic matrix. We call our model TwinNet, a Digital Twin that can accurately estimate relevant SLA metrics for network optimization. TwinNet can generalize to its input parameters, operating successfully in topologies, routing, and queueing configurations never seen during training. We evaluate TwinNet over a wide variety of scenarios with synthetic traffic and validate it with real traffic traces. Our results show that TwinNet can provide accurate estimates of end-to-end path delays in 106 unseen real-world topologies, under different queuing configurations with a Mean Absolute Percentage Error (MAPE) of 3.8%, as well as a MAPE of 6.3% error when evaluated with a real testbed. We also showcase the potential of the proposed model for SLA-driven network optimization and what-if analysis., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/, Spain10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), Spain and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia, Spain and the European Social Fund., Peer Reviewed, Postprint (published version)
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- 2022
38. Optimal power flow computation using neural networks
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Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Cabellos Aparicio, Alberto, Gibert, Karina, Lopez Cardona, Ángela, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Cabellos Aparicio, Alberto, Gibert, Karina, and Lopez Cardona, Ángela
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- 2022
39. FlowDT: A Flow-aware Digital Twin for computer networks
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Ferriol Galmés, Miquel, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Ferriol Galmés, Miquel, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Network modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing them in real production networks. Recently, Graph Neural Networks (GNNs) have emerged as a practical solution to produce network models that can learn and extract complex patterns from real data without making any assumptions. However, state-of-the-art GNN-based network models only work with traffic matrices, this is a very coarse and simplified representation of network traffic. Although this assumption has shown to work well in certain use-cases, it is a limiting factor because, in practice, networks operate with flows. In this paper, we present FlowDT a new DL-based solution designed to model computer networks at the fine-grained flow level. In our evaluation, we show how FlowDT can accurately predict relevant per-flow performance metrics with an error of 3.5%, FlowDT’s performance is also benchmarked against vanilla DL models as well as with Queuing Theory., This work has been supported by the Spanish Government through project TRAINER-A (PID2020-118011GB-C21) with FEDER contribution and the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)
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- 2022
40. Unveiling the potential of graph neural networks for BGP anomaly detection
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Latif Martínez, Hamid, Paillissé Vilanova, Jordi, Yang, Jinze, Cabellos Aparicio, Alberto, Barlet Ros, Pere, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Latif Martínez, Hamid, Paillissé Vilanova, Jordi, Yang, Jinze, Cabellos Aparicio, Alberto, and Barlet Ros, Pere
- Abstract
The Border Gateway Protocol (BGP) is central to the global connectivity of the Internet, enabling fast and efficient dissemination of routing information. Hence, detecting any anomaly concerning BGP announcements is of critical importance to ensure the continuous operation of Internet services. Typically, BGP anomaly detection algorithms have relied on features of the BGP messages, such as the average length of the AS_PATH attribute, the volume of messages, or the type of message (announcement or withdrawal). Even though these algorithms provide good performance, they do not take into account the BGP topology, that is, the graph of ASes created by the BGP announcements. In this paper we investigate if such topology can be useful to predict BGP anomalies. We leverage Graph Neural Networks (GNN), a subset of the Neural Network (NN) family that is designed to process graph-structured data. We propose a GNN model to detect BGP anomalies and study its generalization capability. We compare its performance with two baseline models: a Support Vector Machine (SVM) and a Multilayer Perceptron (MLP), two Machine Learning (ML) techniques used in state-of-the-art solutions. Our GNN model achieves an accuracy of 79.6% using a weakly supervised dataset of 300 anomalies and is able to outperform the two baseline models., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA)., Peer Reviewed, Postprint (author's final draft)
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- 2022
41. Fast traffic engineering by gradient descent with learned differentiable routing
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Almasan Puscas, Felician Paul, Suárez-Varela Maciá, José Rafael, Cholda, Piotr, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Rusek, Krzysztof, Almasan Puscas, Felician Paul, Suárez-Varela Maciá, José Rafael, Cholda, Piotr, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion).This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (˜25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization., This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University and by the PL-Grid Infrastructure. Also, this publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
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- 2022
42. Tunable graphene-based metasurfaces for multi-wideband 6G communications
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Taghvaee, Hamidreza, Pitilakis, Alexandros, Tsilipakos, Odysseas, Tasolamprou, Anna, Kantartzis, Nikolaos V., Kafesaki, Maria, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, Abadal Cavallé, Sergi, Gradoni, Gabriele, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Taghvaee, Hamidreza, Pitilakis, Alexandros, Tsilipakos, Odysseas, Tasolamprou, Anna, Kantartzis, Nikolaos V., Kafesaki, Maria, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, Abadal Cavallé, Sergi, and Gradoni, Gabriele
- Abstract
The next generation of wireless communications within the framework of 6G will be operational at the low THz frequency band. Although THz systems will dramatically enhance several performance indicators such as the data rate, spectral efficiency, and latency, exploiting such technology is challenging. Electromagnetic waves confront severe propagation losses including atmospheric attenuation and diffraction. Thus, such communications are limited to line-of-sight scenarios. In 5G networks, Reconfigurable Intelligent Surfaces (RISs) are intro-duced to solve this issue by redirecting the incident wave toward the receiver and implement virtual-line-of-sight communications. In this paper, we aim to employ this paradigm for 6G networks and design a graphene-based RIS optimized to perform at multiple low atmospheric attenuation channels. We investigate the performance of this multi-wideband design through numerical and analytical analysis., Peer Reviewed, Postprint (author's final draft)
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- 2022
43. RouteNet-Erlang: A graph neural network for network performance evaluation
- Author
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Ferriol Galmés, Miquel, Rusek, Krzysztof, Suárez-Varela Maciá, José Rafael, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Wu, Bo, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Ferriol Galmés, Miquel, Rusek, Krzysztof, Suárez-Varela Maciá, José Rafael, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Wu, Bo, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
- Published
- 2022
44. ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning
- Author
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Almasan Puscas, Felician Paul, Xiao, Shihan, Cheng, Xiangle, Shi, Xiang, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Almasan Puscas, Felician Paul, Xiao, Shihan, Cheng, Xiangle, Shi, Xiang, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Wide Area Networks (WAN) are a key infrastructure in today’s society. During the last years, WANs have seen a considerable increase in network’s traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer’s Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage the network’s resources. However, WAN’s traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL’s solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 s on average for topologies up to 100 links., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/ 10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (published version)
- Published
- 2022
45. Accelerating deep reinforcement learning for digital twin network optimization with evolutionary strategies
- Author
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Güemes Palau, Carlos, Almasan Puscas, Felician Paul, Xiao, Shihan, Cheng, Xiangle, Shi, Xiang, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Güemes Palau, Carlos, Almasan Puscas, Felician Paul, Xiao, Shihan, Cheng, Xiangle, Shi, Xiang, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage the DTN to perform different optimization tasks (e.g., Traffic Engineering, Network Planning).Deep Reinforcement Learning (DRL) showed a high performance when applied to solve network optimization problems. In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior. However, DRL scales poorly with the problem size and complexity. In this paper, we explore the use of Evolutionary Strategies (ES) to train DRL agents for solving a routing optimization problem. The experimental results show that ES achieved a training time speed-up of 128 and 6 for the NSFNET and GEANT2 topologies respectively., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)
- Published
- 2022
46. Multiwideband terahertz communications via tunable graphene-based metasurfaces in 6G networks: Graphene enables ultimate multiwideband THz wavefront control
- Author
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Universitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits, Taghvaee, Hamidreza, Pitilakis, Alexandros, Tsilipakos, Odysseas, Tasolamprou, Anna, Kantartzis, Nikolaos V., Kafesaki, Maria, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, Abadal Cavallé, Sergi, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, Universitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits, Taghvaee, Hamidreza, Pitilakis, Alexandros, Tsilipakos, Odysseas, Tasolamprou, Anna, Kantartzis, Nikolaos V., Kafesaki, Maria, Cabellos Aparicio, Alberto, Alarcón Cot, Eduardo José, and Abadal Cavallé, Sergi
- Abstract
The next generation of wireless networks is expected to tap into the terahertz (THz) band (0.1–10 THz) to satisfy the extreme latency and bandwidth density requirements of future applications. However, the development of systems in this band is challenging as THz waves confront severe spreading and penetration losses, as well as molecular absorption, which leads to strong line-of-sight requirements through highly directive antennas. Recently, reconfigurable intelligent surfaces (RISs) have been proposed to address issues derived from non-line-of-sight (non-LoS) propagation, among other impairments, by redirecting the incident wave toward the receiver and implementing virtual-line-of-sight communications. However, the benefits provided by a RIS may be lost if the network operates at multiple bands. In this article, the suitability of the RIS paradigm in indoor THz scenarios for 6G is assessed grounded on the analysis of a tunable graphene-based RIS that can operate in multiple wideband transparency windows. A possible implementation of such a RIS is provided and numerically evaluated at 0.65/0.85/1.05 THz separately, demonstrating that beam steering and other relevant functionalities are realizable with excellent performance. Finally, the challenges associated with the design and fabrication of multiwideband graphene-based RISs are discussed, paving the way to the concurrent control of multiple THz bands in the context of 6G networks., Peer Reviewed, Postprint (author's final draft)
- Published
- 2022
47. Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case
- Author
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Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Almasan Puscas, Felician Paul, Suárez-Varela Maciá, José Rafael, Rusek, Krzysztof, Barlet Ros, Pere, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Almasan Puscas, Felician Paul, Suárez-Varela Maciá, José Rafael, Rusek, Krzysztof, Barlet Ros, Pere, and Cabellos Aparicio, Alberto
- Abstract
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve many relevant optimization problems (e.g., routing) in self-driving networks. However, existing DRL-based solutions applied to networking fail to generalize, which means that they are not able to operate properly when applied to network topologies not observed during training. This lack of generalization capability significantly hinders the deployment of DRL technologies in production networks. This is because state-of-the-art DRL-based networking solutions use standard neural networks (e.g., fully connected, convolutional), which are not suited to learn from information structured as graphs. In this paper, we integrate Graph Neural Networks (GNN) into DRL agents and we design a problem specific action space to enable generalization. GNNs are Deep Learning models inherently designed to generalize over graphs of different sizes and structures. This allows the proposed GNN-based DRL agent to learn and generalize over arbitrary network topologies. We test our DRL+GNN agent in a routing optimization use case in optical networks and evaluate it on 180 and 232 unseen synthetic and real-world network topologies respectively. The results show that the DRL+GNN agent is able to outperform state-of-the-art solutions in topologies never seen during training., This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501- 100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. This work was also supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University and by the PL-Grid Infrastructure., Peer Reviewed, Postprint (author's final draft)
- Published
- 2022
48. On the enabling of multi-receiver communications with reconfigurable intelligent surfaces
- Author
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Taghvaee, Hamidreza, Jain, Akshay, Abadal Cavallé, Sergi, Gradoni, Gabriele, Alarcón Cot, Eduardo José, Cabellos Aparicio, Alberto, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Taghvaee, Hamidreza, Jain, Akshay, Abadal Cavallé, Sergi, Gradoni, Gabriele, Alarcón Cot, Eduardo José, and Cabellos Aparicio, Alberto
- Abstract
The reconfigurable intelligent surface is a promising technology for the manipulation and control of wireless electromagnetic signals. In particular, it has the potential to provide significant performance improvements for wireless networks. However, to do so, a proper reconfiguration of the reflection coefficients of unit cells is required, which often leads to complex and expensive devices. To amortize the cost, one may share the system resources among multiple transmitters and receivers. In this paper, we propose an efficient reconfiguration technique providing control over multiple beams independently. Compared to time-consuming optimization techniques, the proposed strategy utilizes an analytical method to configure the surface for multi-beam radiation. This method is easy to implement, effective and efficient since it only requires phase reconfiguration. We analyze the performance for indoor and outdoor scenarios, given the broadcast mode of operation. The aforesaid scenarios encompass some of the most challenging scenarios that wireless networks encounter. We show that our proposed technique provisions sufficient improvements in the observed channel capacity when the receivers are close to the surface in the indoor office environment scenario. Further, we report a considerable increase in the system throughput given the outdoor environment., The work of Gabriele Gradoni was supported by the Royal Society under Grant INF\R2\192066. This work was supported in part by the European Commission through the H2020 RISE-6G Project under Grant 101017011 and in part by the EPSRC under Grant EP/V048937/1., Peer Reviewed, Postprint (author's final draft)
- Published
- 2022
49. Exploring alternatives to policy search
- Author
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat de Barcelona, Universitat Rovira i Virgili, Cabellos Aparicio, Alberto, Angulo Bahón, Cecilio, Güemes Palau, Carlos, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat de Barcelona, Universitat Rovira i Virgili, Cabellos Aparicio, Alberto, Angulo Bahón, Cecilio, and Güemes Palau, Carlos
- Abstract
The field of Reinforcement Learning (RL) has been receiving much attention during the last few years as a new paradigm to solve complex problems. However, one of the main issues with the current state of the art is their computational cost. Compared with other paradigms such as Supervised learning, RL requires constant interaction with the environment, which is both expensive and hard to parallelize. In this work we explore a more scalable alternative to conventional RL through the use of Evolution Strategies (ES). This consists in iteratively modifying the current solution by adding Gaussian noise to it, evaluating these modifications, and use their score to guide the improvement of the solution. The advantage of ES lies on that creating and evaluating these modifications can be parallelized. After introducing the network routing scenario, we used it to compare how ES performed against PPO, a RL policy gradient method. Ultimately ES took advantage of increasing its number of workers to eventually overtake PPO, training faster while also generating better results overall. However, it was also clear that for this to occur ES must have access to a considerable amount of hardware resources, hence being viable only within high perfomance computing environments.
- Published
- 2022
50. Bioaccumulation assessment of air-breathing mammals: a discussion paper
- Author
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Arnot, Jon, Birk, Barbara, Curtis-Jackson, Pippa, Gobas, Frank A.P.C., Goss, Kai-Uwe, Habekost, Maike, Hirmann, Doris, Bonnomet, Vincent, Hofer, Tim, Jacobi, Sylvia, Krause, Sophia, Laue, Heike, Laurentie, Michel, Aparicio, Alberto Martin, van der Mescht, Morné, Rauert, Caren, Treu, Gabriele, Redman, Aaron, Saunders, Leslie, Verbruggen, Eric, Wania, Frank, and Whalley, Paul
- Published
- 2022
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