A 4-index supersymmetric formalism with minimal spontaneous supersymmetry breaking computes annealed and quenched complexity of stationary points via the Kac-Rice formula.
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A threshold κ=Θ(1/√α) (α=m/n) separates easy collision finding from OGP-based exponential lower bounds against online algorithms in single-layer binary NNs.
Boundary degree as a per-node feature improves epidemic scenario identification accuracy by 19% on realistic contact networks from Tennessee and Virginia.
Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.
High magnetic fields directly enhance the amplitude and correlation length of stripe order in a cuprate superconductor far above the vortex melting transition, indicating a coupling mechanism independent of superconductivity suppression.
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
CMS sets 95% CL upper limits on σ(X)B(X→ZZ) (300 pb–24 fb) and σ(Z')B(Z'→ZH) (0.4 pb–12 fb) for resonances up to 6 TeV in the bbττ channel with no SM deviation observed.
DynPMNNs replace static activations with time-evolving ODEs based on the FitzHugh-Nagumo model, achieve competitive regression performance on California Housing data with fewer parameters than Neural ODEs or CfCs, and are characterized as finite-dimensional solutions in RKBS.
A cycle-based reentry architecture is proposed to guarantee self-model emergence, self-preservation, and prompt-injection immunity in AGI via a D-I loop and a new S-measure of integrated information.
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
citing papers explorer
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Stationary point complexity via minimal supersymmetry breaking
A 4-index supersymmetric formalism with minimal spontaneous supersymmetry breaking computes annealed and quenched complexity of stationary points via the Kac-Rice formula.
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Collision Resistance of Single-Layer Neural Nets
A threshold κ=Θ(1/√α) (α=m/n) separates easy collision finding from OGP-based exponential lower bounds against online algorithms in single-layer binary NNs.
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Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations
Boundary degree as a per-node feature improves epidemic scenario identification accuracy by 19% on realistic contact networks from Tennessee and Virginia.
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A Geometric Measure of Linear Separability for Neural Representations
Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.
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Direct High-Magnetic-Field Coupling to Stripe Order in a Cuprate Superconductor
High magnetic fields directly enhance the amplitude and correlation length of stripe order in a cuprate superconductor far above the vortex melting transition, indicating a coupling mechanism independent of superconductivity suppression.
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline
A self-supervised Degradation Estimation Network estimates parameters for physics-informed noise distributions to generate realistic synthetic low-light data, showing gains on noise replication, enhancement, and detection tasks.
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Study of ZZ and ZH production in the bb$\tau\tau$ final state and search for high-mass spin-0 and spin-1 resonances in proton-proton collisions at $\sqrt{s}$ = 13 TeV
CMS sets 95% CL upper limits on σ(X)B(X→ZZ) (300 pb–24 fb) and σ(Z')B(Z'→ZH) (0.4 pb–12 fb) for resonances up to 6 TeV in the bbττ channel with no SM deviation observed.
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Physics-Modeled Neural Networks
DynPMNNs replace static activations with time-evolving ODEs based on the FitzHugh-Nagumo model, achieve competitive regression performance on California Housing data with fewer parameters than Neural ODEs or CfCs, and are characterized as finite-dimensional solutions in RKBS.
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Beyond Feedforward Networks: Reentry Neural Systems as the Fundamental Basis of Subjecthood and Intrinsic Safety of Next-Generation AGI
A cycle-based reentry architecture is proposed to guarantee self-model emergence, self-preservation, and prompt-injection immunity in AGI via a D-I loop and a new S-measure of integrated information.
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The Mathematics of AI Winters: The mathematical Taxonomy of Paradigm Fragility in AI Winter
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
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Self-Organising Memristive Networks as Physical Learning Systems
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
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