21,552 research outputs found
Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients
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
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
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
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica 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 domÃnio, com o objetivo de identificar os problemas jurÃdicos 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
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
La tecnologÃa Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus caracterÃsticas 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 extraÃdo o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnologÃa. 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 IngenierÃa de Sistemas y Computació
Towards a unified eco-evolutionary framework for fisheries management: Coupling advances in next-generation sequencing with species distribution modelling
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
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
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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