SF-LIFE is a new large-scale simulated multi-modal trajectory dataset for the San Francisco Bay Area generated via agent-based simulation combined with GTFS transit and OSM network data.
(2022) Simheuristics: An Introductory Tutorial
6 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Only 80 of 250 ATT&CK techniques (32%) allow plausible decoy placement by defenders, grouped into Sweep and Seek patterns, with the rest having no suitable defender asset in the attack path.
CASOP is a new framework that uses a modular algorithm repository, semantic descriptions, and a problem taxonomy to synthesize and evaluate over a million valid optimization pipelines for warehouse order fulfillment across seven benchmarks.
Proposes and tests a t-test based method to limit simulations per iteration in local search for the stochastic parallel machine scheduling and stochastic electric vehicle scheduling problems.
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
A tutorial on applying text-to-image generative AI to support conceptual model communication, simulation visualization, education, and multi-scale model interfacing in modeling and simulation.
citing papers explorer
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SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area
SF-LIFE is a new large-scale simulated multi-modal trajectory dataset for the San Francisco Bay Area generated via agent-based simulation combined with GTFS transit and OSM network data.
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Decoys Cannot Go Everywhere: Mapping the Deception Surface in MITRE ATT&CK
Only 80 of 250 ATT&CK techniques (32%) allow plausible decoy placement by defenders, grouped into Sweep and Seek patterns, with the rest having no suitable defender asset in the attack path.
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Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization
CASOP is a new framework that uses a modular algorithm repository, semantic descriptions, and a problem taxonomy to synthesize and evaluate over a million valid optimization pipelines for warehouse order fulfillment across seven benchmarks.
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Simulation Strategies for an Efficient Local Search to solve Stochastic Scheduling Problems
Proposes and tests a t-test based method to limit simulations per iteration in local search for the stochastic parallel machine scheduling and stochastic electric vehicle scheduling problems.
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Ergodicity of Langevin Dynamics and its Discretizations for Non-smooth Potentials
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
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Text-to-Image Generative AI for Modeling and Simulation: Methods, Opportunities, and Applications
A tutorial on applying text-to-image generative AI to support conceptual model communication, simulation visualization, education, and multi-scale model interfacing in modeling and simulation.