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This project investigates whether the emergence and duration of sunspots have a causal effect on social tensions and downstream harm to populations in developing countries. The cor…
We propose a reinforcement learning methodology that combines intrinsic novelty-driven exploration with explicit goal conditioning and extrinsic reward optimization. The core motiv…
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Continual learning systems must remain plastic enough to learn new tasks while stable enough to avoid catastrophic forgetting, but this tradeoff is increasingly constrained by memo…
This project targets a key safeguards challenge: identifying covert fissile production pathways in nuclear fusion reactor settings using antineutrino observations. We will study wh…
We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the…
Estimate achievable X-gate speeds and fidelities for a Yb-171 nuclear qubit using Raman transfer via a long-lived inner-shell J=2 intermediate (with 1 W/cm^2 available), limited by…
We propose the problem of designing a dynamic, continuously-adaptive activation function that embeds continual-learning inductive biases directly into neuron nonlinearity. The targ…
