21,552 research outputs found

    Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients

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    In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed, and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Emergence simulation of cell-like morphologies with evolutionary potential by virtual molecular interactions

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    This study explores the emergence of life through a simulation model approach. The model "Multi-set chemical lattice model" is a model that allows virtual molecules of multiple types to be placed in each lattice cell on a two-dimensional space. This model is capable of describing a wide variety of states and interactions in a limited number of lattice cell spaces, such as diffusion, chemical reaction, and polymerization of virtual molecules. This model is also capable of describing a wide variety of states and interactions even in the limited lattice cell space of 100 x 100 cells. Furthermore it was considered energy metabolism and energy resources environment. It was able to reproduce the "evolution" in which a certain cell-like shapes adapted to the environment survives under conditions of decreasing amounts of energy resources in the environment. This enabled the emergence of cell-like shapes with the four minimum cellular requirements: boundary, metabolism, replication, and evolution, based solely on the interaction of virtual molecules.Comment: arXiv admin note: text overlap with arXiv:2204.0968

    Economia colaborativa

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    A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social. O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação. O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling

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    The establishment of high-throughput sequencing technologies and subsequent large-scale genomic datasets has flourished across fields of fundamental biological sciences. The introduction of genomic resources in fisheries management has been proposed from multiple angles, ranging from an accurate re-definition of geographical limitations of stocks and connectivity, identification of fine-scale stock structure linked to locally adapted subpopulations, or even the integration with individual-based biophysical models to explore life history strategies. While those clearly enhance our perception of patterns at the light of a spatial scale, temporal depth and consequently forecasting ability might be compromised as an analytical trade-off. Here, we present a framework to reinforce our understanding of stock dynamics by adding also a temporal point of view. We propose to integrate genomic information on temporal projections of species distributions computed by Species Distribution Models (SDMs). SDMs have the potential to project the current and future distribution ranges of a given species from relevant environmental predictors. These projections serve as tools to inform about range expansions and contractions of fish stocks and suggest either suitable locations or local extirpations that may arise in the future. However, SDMs assume that the whole population respond homogenously to the range of environmental conditions. Here, we conceptualize a framework that leverages a conventional Bayesian joint-SDM approach with the incorporation of genomic data. We propose that introducing genomic information at the basis of a joint-SDM will explore the range of suitable habitats where stocks could thrive in the future as a function of their current evolutionary potential.Fundação para a Ciência e Tecnollogia - FCT; ARNETinfo:eu-repo/semantics/publishedVersio

    Information-Theoretic GAN Compression with Variational Energy-based Model

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    We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models.Comment: Accepted at Neurips202

    When to be critical? Performance and evolvability in different regimes of neural Ising agents

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    It has long been hypothesized that operating close to the critical state is beneficial for natural, artificial and their evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions, evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard and simple task: for the hard task, agents evolve closer to criticality whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2103.1218
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