Greedy vector balancing on finite unit-vector sets T in R^d achieves norm bound (2/δ_T)^{d-1} independent of sequence length n.
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11 Pith papers cite this work. Polarity classification is still indexing.
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The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
A block coordinate descent method with specialized solvers for routing and resource allocation is developed to minimize maximum delay and energy in multi-hop D2D networks, with experimental energy savings reported.
Anchor PCA recovers a maximal invariant subspace for multi-domain data via PCA on a modified target matrix that trades off explained variance with domain agreement.
Proves Cheeger inequalities for persistent up p-Laplacians on complex inclusions, with reductions for pseudomanifolds and comparisons to graph cases.
PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
An SDP-based framework computes optimal quantum cloning maps via Choi isomorphism, certifies optimality with duality, and extracts Kraus operators for universal, phase-covariant, asymmetric, and entanglement cloning including higher-order cases.
Establishes n^{1-ε}-hardness of approximation for dichromatic number and acyclic number on tournaments, plus polynomial-time approximations for ℓ-dicolorable digraphs and special dense cases.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
A column generation method with interior-point SDP solvers solves the continuous relaxation of exact D-optimal experimental design to identify support and construct near-optimal exact designs for large-scale instances.
A unified framework for determinant dynamics under low-rank perturbations is developed that extends to singular matrices via pseudodeterminant and provides multiplicative decompositions for controllability Gramians.
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Partitioning Neural Co-Variability
The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.