Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Dicey Games characterize optimal strategies and complexity for teams using pairwise or limited shared randomness, proving they can exceed 1/4 win probability in a 4-player matching-pennies game against an adversary.
The authors introduce aspect-aware datasets GoldRiM and SilverRiM for math papers and AchGNN, a heterogeneous GNN that outperforms prior methods by jointly modeling textual semantics, citations, and author lineage across aspects.
Residual reinforcement learning automates map-based ECU calibration to closely match series production references with minimal human intervention.
citing papers explorer
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Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
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Dicey Games: Shared Sources of Randomness in Distributed Systems
Dicey Games characterize optimal strategies and complexity for teams using pairwise or limited shared randomness, proving they can exceed 1/4 win probability in a 4-player matching-pennies game against an adversary.
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Aspect-Aware Content-Based Recommendations for Mathematical Research Papers
The authors introduce aspect-aware datasets GoldRiM and SilverRiM for math papers and AchGNN, a heterogeneous GNN that outperforms prior methods by jointly modeling textual semantics, citations, and author lineage across aspects.
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Production-Ready Automated ECU Calibration using Residual Reinforcement Learning
Residual reinforcement learning automates map-based ECU calibration to closely match series production references with minimal human intervention.