STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
Applied and Computational Harmonic Analysis , volume=
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Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
Logarithmic centroid method recovers adiabatic Kramers scaling in coherence resonance for SRK model with Feller noise despite bathtub effect, identifies strong-noise breakdown, and demonstrates noise-induced transition to functional synchronization in gap-junction coupled systems.
DRIADA supplies a unified Python data model and workflow for single-neuron selectivity and population dynamics analysis, shown on synthetic data, 13-mouse hippocampal imaging, and a toroidal attractor simulation.
citing papers explorer
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STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting
STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
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Concentration Inequalities for Sample Cross-Covariances
Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
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Breakdown of Adiabatic Scaling and Noise-Induced Functional Synchronization in Deeply Quiescent Excitable Systems
Logarithmic centroid method recovers adiabatic Kramers scaling in coherence resonance for SRK model with Feller noise despite bathtub effect, identifies strong-noise breakdown, and demonstrates noise-induced transition to functional synchronization in gap-junction coupled systems.
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DRIADA: A Python Toolkit for Cross-Scale Analysis of Single-Neuron Selectivity and Population Dynamics
DRIADA supplies a unified Python data model and workflow for single-neuron selectivity and population dynamics analysis, shown on synthetic data, 13-mouse hippocampal imaging, and a toroidal attractor simulation.