Exhaustive symbolic regression on mock weak lensing excess surface density data recovers NFW profiles at 5% fractional errors with as few as 20 clusters but favors simpler functions at higher uncertainties because errors are smallest in the outskirts.
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A Simplex Method for Function Minimization
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SCALAR generates conjectures linking optimal QAOA parameters to graph invariants, recovers known periodicity and parameter-transfer properties, and identifies correlations with optimization landscapes across thousands of graphs up to 77 qubits.
ldmppr is an R package providing tools to model, simulate from, and assess goodness-of-fit for location-dependent marked point processes.
NOEMA CO(2-1) data show a nuclear molecular outflow in NGC 3079 offset by 14 pc with velocities -350 to -450 km/s, mass outflow rate 8.82 M_sun/yr, kinetic power 3.8e41 erg/s, and momentum rate 15 times the AGN radiation momentum, indicating an energy-driven jet-powered outflow.
The ECA-NM hybrid optimization produces chemical-diffusive models that reproduce major flame and detonation properties from detailed chemistry while cutting global error by four orders of magnitude and computational cost by two orders relative to genetic algorithms.
Smartphone transillumination imaging paired with a neuroevolution-tuned ensemble model classifies chicken breast myopathies at 82.4% accuracy on 336 fillets, matching costly hyperspectral systems.
A dual-comb spectroscopy system based on electro-optical combs and difference frequency generation achieves 1.1 ppm methane detection in flames and resolves spatial gradients plus dynamic instabilities.
A Wiener model with linear dynamics for actuation followed by a static nonlinearity reproduces experimental PWM-to-thrust responses of variable-pitch propellers with good accuracy under stated assumptions.
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
Policy-based adaptable failure detection combined with energy-efficient allocation for mitigation actions in edge IoT choreographies.
SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.
citing papers explorer
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Constraining dark matter halo profiles with symbolic regression
Exhaustive symbolic regression on mock weak lensing excess surface density data recovers NFW profiles at 5% fractional errors with as few as 20 clusters but favors simpler functions at higher uncertainties because errors are smallest in the outskirts.
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SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis
SCALAR generates conjectures linking optimal QAOA parameters to graph invariants, recovers known periodicity and parameter-transfer properties, and identifies correlations with optimization landscapes across thousands of graphs up to 77 qubits.
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ldmppr: Location Dependent Marked Point Processes in R
ldmppr is an R package providing tools to model, simulate from, and assess goodness-of-fit for location-dependent marked point processes.
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Molecular Outflows in the Nucleus of the Nearby Compton-thick AGN NGC 3079
NOEMA CO(2-1) data show a nuclear molecular outflow in NGC 3079 offset by 14 pc with velocities -350 to -450 km/s, mass outflow rate 8.82 M_sun/yr, kinetic power 3.8e41 erg/s, and momentum rate 15 times the AGN radiation momentum, indicating an energy-driven jet-powered outflow.
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An efficient method based on the evolutionary center algorithm for optimizing chemical-diffusive models for flame acceleration and DDT
The ECA-NM hybrid optimization produces chemical-diffusive models that reproduce major flame and detonation properties from detailed chemistry while cutting global error by four orders of magnitude and computational cost by two orders relative to genetic algorithms.
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MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection
Smartphone transillumination imaging paired with a neuroevolution-tuned ensemble model classifies chicken breast myopathies at 82.4% accuracy on 336 fillets, matching costly hyperspectral systems.
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Dual-comb spectroscopy for the characterization of laboratory flames
A dual-comb spectroscopy system based on electro-optical combs and difference frequency generation achieves 1.1 ppm methane detection in flames and resolves spatial gradients plus dynamic instabilities.
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Nonlinear System Identification of Variable-Pitch Propellers Using a Wiener Model
A Wiener model with linear dynamics for actuation followed by a static nonlinearity reproduces experimental PWM-to-thrust responses of variable-pitch propellers with good accuracy under stated assumptions.
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Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
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Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
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Optimally Self-Healing IoT Choreographies
Policy-based adaptable failure detection combined with energy-efficient allocation for mitigation actions in edge IoT choreographies.
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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.
- Galactic Rotation Curves from Full-Disk Newtonian Modeling: The Lost and Found Framework