QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
Efficiency of coordinate descent methods on huge-scale optimization problems
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SC-DN establishes a global first-order stationary point per round and solves a mixed-integer signomial program to optimize four control variables for VFL, yielding better classification performance and lower resource use than greedy baselines on image and multi-modal data.
Large language models display three universal scale-dependent regimes of behavior—stable, chaotic, and signal-dominated—driven by floating-point rounding errors that produce an avalanche effect in early layers.
citing papers explorer
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QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
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Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
SC-DN establishes a global first-order stationary point per round and solves a mixed-integer signomial program to optimize four control variables for VFL, yielding better classification performance and lower resource use than greedy baselines on image and multi-modal data.
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Numerical Instability and Chaos: Quantifying the Unpredictability of Large Language Models
Large language models display three universal scale-dependent regimes of behavior—stable, chaotic, and signal-dominated—driven by floating-point rounding errors that produce an avalanche effect in early layers.