Two quantum linear system solvers are presented with query complexity independent of the condition number, scaling instead with an effective condition number or a solution-norm ratio.
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15 Pith papers cite this work, alongside 4,269 external citations. Polarity classification is still indexing.
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Larval connectome rate operator shows gross behavior fixed by degree and weight statistics while input routing and dominant driving modes are fixed by exact wiring placement.
Introduces downward conditional monotonicity for MMPP to obtain stochastic domination bounds that determine survival and extinction regimes for contact processes in finite-state random environments via QBD eigenvalue comparison.
Introduces a scalable algebraic framework relating rank deficiency of generalized Vandermonde matrices for sparse steering vectors to thinned Toeplitz matrices and augmented full-ULA matrices to characterize and avoid multi-source ambiguities in thinned uniform linear arrays.
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.
CI* (normalized −c3 of the characteristic polynomial) is the unique natural scaling of that coefficient that preserves average consistency under leave-one-out submatrices, hence is size-independent.
Convex reformulation and polynomial-time algorithm for spectral design problems that update a prior information matrix by rank-one updates under Euclidean-norm bounds on the design vectors.
Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
Any spectral diagnostic that depends only on singular values or the symmetric part of a degree-normalized attention matrix is invariant under transpose, so it cannot see the direction of information flow.
Classifies faces of copositive and completely positive cones over the second-order cone, examines dimension and exposedness, and computes two chain-related parameters.
citing papers explorer
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Faster quantum linear system solver beyond the condition number
Two quantum linear system solvers are presented with query complexity independent of the condition number, scaling instead with an effective condition number or a solution-norm ratio.
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A frozen rate operator from the complete larval connectome: degree and weight govern the gross response, exact wiring governs input routing and mushroom-body modes
Larval connectome rate operator shows gross behavior fixed by degree and weight statistics while input routing and dominant driving modes are fixed by exact wiring placement.
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Downward conditional monotonicity gives survival and extinction for contact processes in random environments
Introduces downward conditional monotonicity for MMPP to obtain stochastic domination bounds that determine survival and extinction regimes for contact processes in finite-state random environments via QBD eigenvalue comparison.
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Ambiguity Analysis and Design of Sparse Arrays via Generalized Vandermonde Rank Conditions
Introduces a scalable algebraic framework relating rank deficiency of generalized Vandermonde matrices for sparse steering vectors to thinned Toeplitz matrices and augmented full-ULA matrices to characterize and avoid multi-source ambiguities in thinned uniform linear arrays.
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
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Generalizing Reduced Rank Extrapolation to Low-Rank Matrix Sequences
Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.
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Size independence of consistency index for pairwise comparison matrices in analytic hierarchy process
CI* (normalized −c3 of the characteristic polynomial) is the unique natural scaling of that coefficient that preserves average consistency under leave-one-out submatrices, hence is size-independent.
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Optimal Spectral Design with Prior Information
Convex reformulation and polynomial-time algorithm for spectral design problems that update a prior information matrix by rank-one updates under Euclidean-norm bounds on the design vectors.
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Stimulus symmetries can confound representational similarity analyses
Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.
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Harnessing Floating Car Data, Traffic Camera Observations, and Network Flow Analysis for Traffic Volume Estimation
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
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Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics
Any spectral diagnostic that depends only on singular values or the symmetric part of a degree-normalized attention matrix is invariant under transpose, so it cannot see the direction of information flow.
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Facial structure of copositive and completely positive cones over a second-order cone
Classifies faces of copositive and completely positive cones over the second-order cone, examines dimension and exposedness, and computes two chain-related parameters.
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