Exploring topics through academic research and investigation
We describe Ringside Analytics, an end-to-end machine-learning system trained on 40+ years of professional wrestling data — 482,166 matches and 731,133 wrestler-match par- ticipations spanning WWE, AEW, WCW, ECW, NXT, and TNA.
We additionally train a Deep Q-Network (DQN) policy through adversarial co-evolutionary training, where the defense adapts against an attacker that continuously retrains on protected data. We evaluate CANO against five baseline strategies (Gaussian, FGSM, PGD, Carlini-Wagner, and Laplace noise) across 12 datasets (including the real FP-Stalker browser-fingerprint corpus — 776 users, 13,674 fingerprints), 3 attack models, and 6 noise budgets, totaling 68,885 experimental configurations (54,281 in-scope after excluding the 2-user cybersec_intrusion dataset).
A four-part research series quantifying the full cost of AI data center deployment — from global site selection to hardware lifecycle to device-level TCO. Using Monte Carlo simulation, machine learning, and real government datasets, the research demonstrates that Nordic locations are 75% cheaper than traditional US hubs, hardware energy costs dwarf facility costs by 5–10×, and climate change compounds that gap every year through 2050. Includes full Python source code, reproducible notebooks, and supporting cost models for hardware tiers and Apple ARM devices.
Private equity ownership of essential consumer services produces measurable harm when aggressive leverage meets weak federal oversight.
TL;DR: Our LSTM predicts ISS position to within 125 km at 6 hours (54.5 km at 1 hour) — 10x better than physics-based propagation. Adding solar wind data via cross-attention improves predictions by 17% during geomagnetic storms.