16,564 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
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
We study how a principal can efficiently and effectively intervene on the
rewards of a previously unseen learning agent in order to induce desirable
outcomes. This is relevant to many real-world settings like auctions or
taxation, where the principal may not know the learning behavior nor the
rewards of real people. Moreover, the principal should be few-shot adaptable
and minimize the number of interventions, because interventions are often
costly. We introduce MERMAIDE, a model-based meta-learning framework to train a
principal that can quickly adapt to out-of-distribution agents with different
learning strategies and reward functions. We validate this approach
step-by-step. First, in a Stackelberg setting with a best-response agent, we
show that meta-learning enables quick convergence to the theoretically known
Stackelberg equilibrium at test time, although noisy observations severely
increase the sample complexity. We then show that our model-based meta-learning
approach is cost-effective in intervening on bandit agents with unseen
explore-exploit strategies. Finally, we outperform baselines that use either
meta-learning or agent behavior modeling, in both -shot and -shot
settings with partial agent information
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
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
Bayesian Reconstruction of Magnetic Resonance Images using Gaussian Processes
A central goal of modern magnetic resonance imaging (MRI) is to reduce the
time required to produce high-quality images. Efforts have included hardware
and software innovations such as parallel imaging, compressed sensing, and deep
learning-based reconstruction. Here, we propose and demonstrate a Bayesian
method to build statistical libraries of magnetic resonance (MR) images in
k-space and use these libraries to identify optimal subsampling paths and
reconstruction processes. Specifically, we compute a multivariate normal
distribution based upon Gaussian processes using a publicly available library
of T1-weighted images of healthy brains. We combine this library with
physics-informed envelope functions to only retain meaningful correlations in
k-space. This covariance function is then used to select a series of
ring-shaped subsampling paths using Bayesian optimization such that they
optimally explore space while remaining practically realizable in commercial
MRI systems. Combining optimized subsampling paths found for a range of images,
we compute a generalized sampling path that, when used for novel images,
produces superlative structural similarity and error in comparison to
previously reported reconstruction processes (i.e. 96.3% structural similarity
and <0.003 normalized mean squared error from sampling only 12.5% of the
k-space data). Finally, we use this reconstruction process on pathological data
without retraining to show that reconstructed images are clinically useful for
stroke identification
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making
under uncertainty, with applications including multi-arm bandits, active
learning, and black-box optimization. Bayesian optimization selects decisions
(i.e. objective function queries) with maximal expected utility with respect to
the posterior distribution of a Bayesian model, which quantifies reducible,
epistemic uncertainty about query outcomes. In practice, subjectively
implausible outcomes can occur regularly for two reasons: 1) model
misspecification and 2) covariate shift. Conformal prediction is an uncertainty
quantification method with coverage guarantees even for misspecified models and
a simple mechanism to correct for covariate shift. We propose conformal
Bayesian optimization, which directs queries towards regions of search space
where the model predictions have guaranteed validity, and investigate its
behavior on a suite of black-box optimization tasks and tabular ranking tasks.
In many cases we find that query coverage can be significantly improved without
harming sample-efficiency.Comment: For code, see
https://www.github.com/samuelstanton/conformal-bayesopt.gi
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
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