most citedNeural Network-Driven Volatility Drag Mitigation under Aggressive Leverage

1 citations

9 papers

cs.LG2026

CutClean: Neural Network Pruning for Privacy-Preserving Inference

Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise +1

Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of repr…

quant-ph2026

Nanoscale graphitization and defect evolution in silicon-vacancy center-containing nanodiamonds under high-pressure high-temperature annealing

M. De Feudis, B. Yavkin, L. Henry +6

Group-IV color centers, such as the silicon-vacancy (SiV) defect, are highly promising for solid-state quantum technologies. However, nanodiamonds typically exhibit significant lat…

q-fin.PM20261 cited

Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage

Christian Bongiorno, Efstratios Manolakis, Rosario Nunzio Mantegna

This paper introduces a compact reformulation of a modular end-to-end neural network for global minimum-variance portfolio optimization that decouples model complexity from both lo…

math.DS2026

Strong Stability of Linear Functional Equations with Distributed Delays

Yacine Chitour, Felipe Gonçalves Netto, Guilherme Mazanti

This paper considers linear functional equations on with distributed delays defined by matrix-valued measures of bounded variation. More precisely, we are interested…

cs.GT2026

Positional Determinacy with Colored Vertices: a 1-to-2-Player Lift

Raphaël Berthon, Stéphane Le Roux

Positional determinacy of vertex-colored parity games was proved in the 1990s, which directly implies positional determinacy of edge-colored parity games. In 2006, it was shown tha…

cs.LO2026

Some Results on Causal Modalities in General Spacetimes

Marco Lewis, Nesta van der Schaaf

Causality is one of the fundamental structures of spacetimes, determining the possible behaviour and propagation of physical information. Causal structure can be analysed through t…