Results 81 to 90 of about 1,909,109 (209)

Dynamic Resource Allocation Optimisation and Security‐Resilient Control for Bandwidth‐Limited Network Control Systems With Data Conflicts

open access: yesCAAI Transactions on Intelligence Technology, Volume 11, Issue 3, Page 920-934, June 2026.
ABSTRACT Networked control systems (NCSs) often suffer from performance degradation due to limited communication bandwidth, which can cause data transmission conflicts and packet loss. Existing scheduling strategies may fail to simultaneously meet the real‐time requirements and the importance of multisensor data, and they are particularly vulnerable ...
Da Chen   +5 more
wiley   +1 more source

Assortative Matching on Income

open access: yesEconometrica, Volume 94, Issue 3, Page 957-989, May 2026.
We analyze marital matching on income using an extremely rich Dutch data set containing all income tax files over seven years. We develop a novel methodology that directly extends previous contributions to allow for highly flexible matching patterns. Investigating all marriages that took place between 2013 and 2019, we find that marital patterns are ...
Pierre‐André Chiappori   +3 more
wiley   +1 more source

An Approximation Algorithm for Risk-Averse Submodular Optimization [PDF]

open access: yes, 2020
Whole version for WAFR, 2018 final ...
Lifeng Zhou 0001, Pratap Tokekar
openaire   +3 more sources

Online Submodular Maximization via Online Convex Optimization [PDF]

open access: yes
We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, threshold potential functions, reduces to online convex optimization ...
Ioannidis, Stratis   +4 more
core   +2 more sources

Online Weakly DR-Submodular Optimization Under Stochastic Cumulative Constraints

open access: yesTsinghua Science and Technology
In this paper, we study a class of online continuous optimization problems. At each round, the utility function is the sum of a weakly diminishing-returns (DR) submodular function and a concave function, certain cost associated with the action will occur,
Junkai Feng   +3 more
doaj   +1 more source

A Universal Meta‐Heuristic Framework for Influence Maximisation in Hypergraphs

open access: yesCAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 396-410, April 2026.
ABSTRACT Influence maximisation (IM) aims to select a small number of nodes that are able to maximise their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in simple networks, higher‐order interactions between entities in various real‐world systems are usually not taken into ...
Ming Xie   +5 more
wiley   +1 more source

Same/Other/All K‐Fold Cross‐Validation for Estimating Similarity of Patterns in Data Subsets

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.
ABSTRACT In many real‐world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc.).
Toby Dylan Hocking   +5 more
wiley   +1 more source

Game‐Theoretic Multi‐Agent Dynamic Traffic Assignment Using Congestion‐Aware Adaptive Learning

open access: yesIET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
This paper proposes a novel game‐theoretic multi‐agent framework for dynamic traffic assignment problems, integrating congestion‐aware adaptive learning to simultaneously reduce individual travel time and improve overall network efficiency under dynamic congestion.
Dandan Wu   +4 more
wiley   +1 more source

Reinforcement Learning–Enhanced Influence Maximisation via Credibility‐Based Clustering and Metaheuristic Optimisation

open access: yesIET Networks, Volume 15, Issue 1, January/December 2026.
In this study, a novel approach of integrating artificial intelligence (AI) and machine learning, that is, reinforcement learning (RL), with centrality measures is introduced to improve information spreading in social networks. RL models the decision process to dynamically select powerful nodes that bypass static measures of centrality constraints ...
Mohammad Mehdi Karimi   +3 more
wiley   +1 more source

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