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When the grid is under stress, the real question is not just how much power exists, but how reliably it can be delivered. That is the problem CapOptix targets. Traditional capacity market designs often rely on expected-value measures of energy unserved, which can miss the risk exposure in a system facing renewable intermittency, demand swings, and price shocks. CapOptix treats capacity commitments as reliability options, a type of financial derivative tied to wholesale electricity prices. It then uses a Markov regime switching process to account for structural price shifts, where prices can move between different market states. The authors apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms. The result is a new way to think about what reliability should cost when energy markets are under pressure.
If a brain-computer interface has to learn from your EEG, it usually struggles when it meets a new person. This paper tackles that problem without touching the original training data, which matters when privacy and practical deployment both matter. The authors introduce FUSED, a framework that pairs a large EEG foundation model — a model pretrained on large-scale data — with a compact specialist model for source-free domain adaptation, meaning the system adapts to an unlabeled target user without access to source data. FUSED uses two branches with linear and prototype views to generate pseudo-labels, then filters samples by consensus, refines labels in two stages, and calibrates the foundation model before distilling its knowledge into the specialist model. Across three EEG paradigms — motor imagery, emotion recognition, and SSVEP — the framework delivers consistent state-of-the-art performance. The result is a more robust route to cross-subject EEG decoding that keeps source data out of the loop.
If you want neural circuits that settle down instead of wandering forever, this paper points to two extreme time scales. A linear-threshold network is a simple model of interacting neuron populations, with a dissipative part that pulls activity back and a recurrent part that feeds signals around the network. The authors build a one-parameter family of these networks that keeps the same equilibrium set and preserves a structural condition called Lyapunov diagonal stability. In the fast limit, the family becomes a projected dynamical system, and the paper proves that this limit is globally exponentially stable. In the slow limit, it becomes a discontinuous hard-selector system, and the paper proves that limit is globally asymptotically stable. The message is that the endpoints are not just mathematical curiosities: they capture the stability mechanisms of the whole family. The authors combine these proofs with numerical evidence and argue that checking the fast and slow limits may offer a structured path toward global stability for LTNs with asymmetric interactions and heterogeneous dissipation.
