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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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7.9%15.8%23.8%31.7% · Nov 201919922001200920182026
48 results for real root classification

Develops a method to identify nonproperness sets in likelihood-equation systems.

problem Classifying data based on the number of positive critical points of likelihood functions.
method Computes nonproperness sets using a novel method and proves its correctness.
result The method is more efficient than existing methods in the literature.

New proof classifies homogeneous 3-Sasakian and quaternionic Kähler manifolds.

problem Classifying homogeneous 3-Sasakian and quaternionic Kähler manifolds.
method Constructing an explicit one-to-one correspondence via root systems and analyzing properties of real projective spaces.
result Derivation of the classification of homogeneous positive quaternionic Kähler manifolds.

In this article, relations between the root space decomposition of a Riemannian symmetric space of compact type and the root space decompositions of its totally geodesic submanifolds (symmetric subspaces) are described. These relations provide an approach to the classification of totally geodesic submanifolds in Rieman…

2006-03-07abs ↗pdf ↗

Study differential properties of matrix square roots in specific cases.

problem Understanding matrix square roots in semi-simple, symmetric, and orthogonal cases.
method Analysis of differential and metric structures of real square roots of matrices under specific conditions.
result Differential properties of matrix square roots in semi-simple, symmetric, and orthogonal cases.

Automorphisms of Lie algebras and their root systems are fully lifted.

problem Understanding automorphisms of real semisimple Lie algebras and their root systems.
method Proving every automorphism of the restricted root system can be lifted to a Lie algebra automorphism.
result Automorphisms of restricted root systems can be fully lifted to Lie algebras.

By making use of the classification of real simple Lie algebra, we get the maximum of the squared length of restricted roots case by case, thus we get the upper bounds of sectional curvature for irreducible Riemannian symmetric spaces of compact type. As an application, we verify Sampson's conjecture in all cases for i…

2005-05-29abs ↗pdf ↗

The paper extends ternary algebra concepts using cube roots of unity.

problem Extending algebraic structures from binary to ternary multiplication.
method Introducing ternary associator, commutator, and Lie algebra at cube roots of unity.
result Derived an identity for ternary commutator based on GA(1,5)GA(1,5).

New framework for root-cause analysis in complex CPSs using spatiotemporal graphical modeling.

problem Anomaly detection and root-cause analysis in complex cyber-physical systems (CPSs).
method Spatiotemporal graphical modeling based on symbolic dynamics.
result Approaches S3S^3 and A3A^3 achieve high accuracy in root-cause analysis under various fault scenarios.

A novel method classifies shapes by their square-root velocity function.

problem Classifying shapes in infinite-dimensional, curved spaces.
method Square-root velocity function, tangent spaces, principal components, combining pairwise classifiers.
result Improves classification accuracy by separating shapes and reducing dimensionality.

Let CC be a curve in a closed orientable surface FF of genus g2g \geq 2 that separates FF into subsurfaces Fi~\widetilde {F_i} of genera gig_i, for i=1,2i = 1,2. We study the set of roots in $\Mod(F)$ of the Dehn twist tCt_C about CC. All roots arise from pairs of CniC_{n_i}-actions on the Fi~\widetilde{F_i}, where $n=\l…

2011-04-05abs ↗pdf ↗

The paper explores feature selection for improving classification accuracy in event logs.

problem Improving machine learning-based interactive root cause analysis for business process instances.
method Developed structural features from event logs and compared six feature selection algorithms.
result Feature selection can improve classification accuracy without significantly increasing run-time.

Continuity of roots of hyperbolic polynomials with smooth coefficients.

problem Continuity of the solution map for hyperbolic polynomials.
method Proving continuity of the solution map from hyperbolic polynomials of degree d with C^d coefficients to their increasingly ordered roots.
result Continuity of the solution map for hyperbolic polynomials with C^d coefficients.

We introduce a remarkable subset "the stem" of the set of positive roots of a reduced root system. The stem determines several interesting decompositions of the corresponding reductive Lie algebra. It gives also a nice simple three dimensional subalgebra and a "Cayley transform". In the present paper we apply the above…

2010-05-02abs ↗pdf ↗

The paper explores how polynomial roots and operator eigenvalues change with parameters.

problem How do roots of polynomials and eigenvalues of operators vary with parameter changes?
method Analyzes parameter dependence of polynomials and linear operators, covering real analytic to differentiable of finite order.
result Definitive optimal results for perturbation theory of polynomials and linear operators, including hyperbolic polynomials.

We describe a class (called regular) of invariant generalized complex structures on a real semisimple Lie group G. The problem reduces to the description of admissible pairs (\gk, ω), where \gk is an appropriate regular subalgebra of the complex Lie algebra \gg^{C} associated to G and ωis a closed 2-form on \gk, such t…

2010-09-06abs ↗pdf ↗

Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.

problem Anomalies in dynamical systems impact performance and reliability.
method Proposes ICODE Networks for anomaly detection, RCA, and type classification.
result Demonstrates the ability to accurately detect anomalies, classify types, and pinpoint origins.

The study examines generalization bounds for regression and classification tasks on adaptive input domains.

problem Understanding the generalization error in adaptive input domains for regression and classification.
method The analysis considers regression and classification separately, using Lipschitz continuity and 2-norm/0/1 loss for measurement. It also highlights the polynomial relationship between generalization bounds and network parameters.
result Generalization bounds for regression and classification are inversely proportional to a polynomial of the number of parameters, emphasizing the advantages of over-parameterized networks.

Probability calibration trees improve accuracy of probability estimates.

problem Improving accuracy and calibration of probability estimates from classifiers.
method Probability calibration trees modify logistic model trees to learn different models in regions of the input space.
result Probability calibration trees outperform isotonic regression and Platt scaling in terms of root mean squared error.

A new deep metric learning method for defect classification in threaded pipe connections.

problem Defect classification in threaded pipe connections with limited and imbalanced multichannel functional data.
method COMPILED approach based on deep metric learning for imbalanced, multichannel, and partially observed functional data.
result Superior accuracy compared to existing benchmarks in a real-world case study.

We show that a generic real projective n-dimensional hypersurface of degree 2n-1 contains "many" real lines, namely, not less than (2n-1)!!, which is approximately the square root of the number of complex lines. This estimate is based on the interpretation of a suitable signed count of the lines as the Euler number of …

2012-01-13abs ↗pdf ↗

FID misaligns with human judgment due to reliance on ImageNet classes.

problem FID metric's reliance on ImageNet classes causes discrepancies with human evaluation.
method Investigated and visualized the feature space of FID and its relation to ImageNet classes.
result Aligning histograms of Top-NN ImageNet classifications can reduce FID without improving quality.

Paper establishes a universal growth rate for smooth surrogate losses in classification.

problem Analyzing growth rates of consistency bounds for various surrogate losses.
method Proves square-root growth rate for smooth margin-based losses; extends to multi-class classification.
result Demonstrates a universal square-root growth rate for smooth comp-sum and constrained losses.

The study confirms that market volatility can be explained by correlated metaorders impacting prices in a square-root fashion.

problem Explaining market volatility using metaorders and their impact.
method Generated synthetic market data and analyzed the correlation between order flow and returns.
result The square-root law of market impact is confirmed and can be measured from anonymized trade data.

Logitron combines Perceptron and logistic loss for improved classification.

problem Non-convex and non-smooth zero-one loss function in classification models.
method Introduces a Perceptron-augmented convex classification framework with an extended logistic loss function.
result Hinge-Logitron outperforms logistic regression and SVM in classification accuracy.

Anomaly detection aids in labeling fast-running processes for machine learning.

problem Manual labeling of fast-running processes for machine learning models.
method Anomaly detection to assist in labeling data, specific metrics for model validation.
result Possibility to manually classify data for training machine learning models.

Compact bilinear pooling approximates covariance features for faster training.

problem Efficiently approximating covariance features for faster training.
method Compact bilinear pooling extended to polynomial approximations of covariance features.
result The proposed method achieves comparable accuracy with fewer dimensions.

Study on Einstein metrics on specific homogeneous spaces.

problem Existence of invariant Einstein metrics on aligned homogeneous spaces.
method Analysis of G_1xG_2-invariant Einstein metrics on G_1/K x G_2/K for compact Lie groups.
result Existence of Einstein metrics is equivalent to a real root of a quartic polynomial.