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cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
El Nino Southern Oscillation and Atlantic Multidecadal Os...
Suchit Basineni · 2024-10-06 · via cs.LG updates on arXiv.org

Tropical cyclones (TCs), including hurricanes and typhoons, cause significant property damage and result in fatalities, making it crucial to understand the factors driving extreme TCs. The El Nino Southern Oscillation (ENSO) influences TC formation through tropospheric vorticity, wind shear, and atmospheric circulations. Apart from atmospheric changes, oceans influence activity through sea surface temperatures (SSTs) and deep ocean heat content. These Atlantic SSTs determine the Atlantic Multidecadal Oscillation (AMO), which indicates SST variability in the Atlantic. This research focuses on ENSO, AMO, and SSTs impact on the strength and frequency of TCs in the North Atlantic Basin. AMO and SST anomalies are increasing at an alarming rate, but it remains unclear how their dynamics will influence future TC behavior. I used observational cyclone track data from 1950 to 2023, the Oceanic Nino Index (ONI), and NOAAs Extended Reconstructed SST V5 (ERSST). I found that Increasing SSTs over the past decade indicate stronger TCs, while warm phase AMO periods correspond with higher TC frequency. Meanwhile, a greater frequency of landfalling TCs can be attributed to La Nina or ENSO-neutral, with El Nino decreasing the frequency of landfalling TCs. Such relationships suggest that as the seasonal predictability of ENSO and SSTs improve, seasonal TC forecasts may improve.