New ML method detects incomplete bid-rigging cartels.
problem Detecting incomplete bid-rigging cartels in competitive bidding.
method Combines statistical screens with machine learning.
result Algorithm outperforms existing methods in incomplete cartels.
Study of α-associated metrics on null hypersurfaces.
problem Developing a method to construct α-associated metrics on null hypersurfaces. method Introduce and study α-associated metrics induced by a non-vanishing function α on a rigging vector field. result Constructive method to find α-associated metrics with Levi-Civita connections matching given null hypersurface. RIG extends IG to Riemannian manifolds for explainable AI.
problem Lack of explainability in AI models.
method Extension of Integrated Gradients to Riemannian manifolds.
result RIG restricts to IG in Euclidean space.
Rigging technique introduced in \cite{bi0} is a convenient way to address the study of null hypersurfaces. It offers in addition the extra benefit of inducing a Riemannian structure on the null hypersurface which is used to study geometric and topological properties on it. In this paper we develop this technique showin…
Deep learning detects bid-rigging cartels with high accuracy.
problem Detecting bid-rigging cartels using pairwise bidding interactions.
method Convolutional neural networks applied to graphs of normalized bid values.
result Convolutional neural networks achieve around 90% accuracy in classifying collusive and competitive bidding interactions.
Enhances Koopman operator estimation with intrinsic observables in RKHS.
problem Accurate estimation of Koopman operator and its spectrum.
method Jet Extended Dynamic Mode Decomposition (JetEDMD) leveraging RKHS jets.
result Proves JetEDMD's superiority with error bounds and convergence rate.
Study null hypersurfaces in Lorentzian manifolds, proving Riemannian flow structure.
problem Properties of Lorentzian manifolds influenced by totally geodesic null hypersurfaces.
method Coupling rigging technique with null foliation existence to prove Riemann flow structure.
result Proves curvature conditions restrict causal structure of spacetime.
The author discusses in some detail the old definitions of the curvature tensors for rigged metrized distributions on manifolds given by Schouten, Wagner, and Solov'ev. To calculate the Solov'ev sectional and Ricci curvatures for homogeneous sub-Riemannian manifolds, the author suggests to use in some cases special rig…
Unified approach to conformal and modular invariants on surfaces.
problem Constructing a general family of conformal invariants on surfaces.
method Using an identification of Teichmüller space and rigged moduli space, and analytic work on harmonic functions.
result Unified conformal and modular invariants can be viewed as generalized modular invariants and functions on the rigged moduli space.
Develops 2-Hilbert spaces for bundle gerbes in geometric quantization.
problem Higher geometric quantization of bundle gerbes.
method Introduces 2-Hilbert spaces, rig-categories, and duals for bundle gerbes.
result Constructs a 2-Hilbert space of sections for bundle gerbes.
Abstract: Defines differential equations in tangent categories, providing conditions for completeness and new perspectives.
problem Defining and working with differential equations in abstract tangent categories.
method Introduces curve objects and dynamical systems, providing conditions for completeness and exploring exponential maps.
result Provides abstract conditions for dynamical systems to be complete and introduces differential exponential rig.
Using the classification of transitive groups we classify indecomposable quandles of size <36. This classification is available in Rig, a GAP package for computations related to racks and quandles. As an application, the list of all indecomposable quandles of size <36 not of type D is computed.
Quandle colorings and cocycle invariants are studied for composite knots, and applied to chirality and abelian extensions. The square and granny knots, for example, can be distinguished by quandle colorings, so that a trefoil and its mirror can be distinguished by quandle invariants of composite knots. We investigate t…
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
problem Fault detection in planetary gearboxes using vibration signals.
method Ensemble of three convolution kernel-based methods (ROCKET, 1D CNN with ResNet, FCN).
result Outperforms other approaches with over 98.8% accuracy.
The purpose of the present work is to study (marginally) trapped submanifolds lying in a null hypersurface. Let $(M,g,N)\to\Bm(c)$ be a null hypersurface of a space-time with constant sectional curvature c, endowed with a Screen Integrable and Conformal rigging N. The (Marginally) Trapped Submanifolds we are intere…
Deep learning improves oilfield equipment maintenance and reduces downtime.
problem Predicting equipment failure in oilrigs to minimize downtime.
method Developed and tested neural networks on oilfield datasets, using data processing and feature extraction.
result Deep learning can predict oilfield equipment failure with reduced downtime.
Deep learning detects icebergs and ships from SAR data.
problem Detecting icebergs and ships from SAR data for Arctic navigation safety.
method Transfer Learning with a CNN, augmented data, and multiple outputs.
result Significant accuracy boost (logarithmic score 0.1463) in iceberg and ship detection.
Method trains sparse neural networks without sacrificing accuracy.
problem Training sparse neural networks limits model size.
method Updates sparse network topology during training.
result Requires fewer FLOPs to achieve accuracy.
Synthetic framework for null hypersurfaces in non-smooth spacetimes.
problem Analyzing null hypersurfaces in non-smooth spacetimes.
method Develops synthetic null hypersurfaces using optimal transport and Lorentzian geometry.
result Synthetic null energy condition stabilizes under convergence and applies to low-regularity spacetimes.
Study monitors wind turbine drivetrain bearings using dictionary learning from vibration data.
problem Early detection of faults in wind turbine drivetrain bearings with minimal false positives.
method Unsupervised dictionary learning from 46 months of vibration data.
result Abnormal dictionary adaptation signals faults 6-12 months before bearing or gearbox replacement.