Herring is the first parallel γ-batch-order-fairness DAG BFT protocol that achieves higher saturation throughput than FairDAG-RL and DoD-W by parallelizing graph construction and piggybacking missing edge resolution on reliable broadcast.
Jensen, Zhenli Sheng, and Bin Yang
5 Pith papers cite this work. Polarity classification is still indexing.
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SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
KGI-Bench evaluates data integration pipelines into knowledge graphs using coverage, correctness, and consistency metrics on movie domain datasets with 12 pipelines tested.
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.
citing papers explorer
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Herring: Parallel Batch-Order-Fairness on DAG-based Blockchain Consensus
Herring is the first parallel γ-batch-order-fairness DAG BFT protocol that achieves higher saturation throughput than FairDAG-RL and DoD-W by parallelizing graph construction and piggybacking missing edge resolution on reliable broadcast.
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Benchmarking Sensor-Fault Robustness in Forecasting
SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
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Evaluation of Pipelines for Data Integration into Knowledge Graphs
KGI-Bench evaluates data integration pipelines into knowledge graphs using coverage, correctness, and consistency metrics on movie domain datasets with 12 pipelines tested.
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DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
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PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.