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.
Quantum-enhanced method improves stock return prediction accuracy.
Paper proposes a new method for learning business process representations.
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…
Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-deman…
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …
Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time serie…
New method constructs axial vector fields and defines quasi-local spin-angular momentum.
Reinforcement learning can interact with the environment and is suitable for applications in decision control systems. Therefore, we used the reinforcement learning method to establish a foreign exchange transaction, avoiding the long-standing problem of unstable trends in deep learning predictions. In the system desig…
Improved robustness of 1D CNNs for heart arrhythmia classification.
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of random observed and unobserved pairs, with a number of samples that grows quadratically with the corpus size, making it expensive to scale to…
The paper derives theorems about curl eigenfields on a 3-sphere using angular momentum theory.
We define quasi-local conserved quantities in general relativity by using the optimal isometric embedding in [26] to transplant Killing fields in the Minkowski spacetime back to the 2-surface of interest in a physical spacetime. To each optimal isometric embedding, a dual element of the Lie algebra of the Lorentz group…
Deep learning transforms time series into images for anomaly detection in industrial assets.
Study forecasts U.S. bond index using deep learning, finding persistence is key.
The angular power spectrum characterizes neural network complexity.
New method differentiates square-root Kalman filters robustly.
This paper presents generalized momentum mappings for covariant Hamiltonian field theories. The new momentum mappings arise from a generalization of symplectic geometry to , the bundle of vertically adapted linear frames over the bundle of field configurations . Specifically, the generalized field momentum obs…
Develops a universal Hermitian projective calculus for complex hyperbolic two-space
The most general formulation of Penrose's inequality yields a lower bound for ADM mass in terms of the area, charge, and angular momentum of black holes. This inequality is in turn equivalent to an upper and lower bound for the area in terms of the remaining quantities. In this note, we establish the lower bound for a …
We show that a stationary solution of the Einstein-Maxwell equations which is close to a non-degenerate Reissner-Nordström-de Sitter solution is in fact equal to a slowly rotating Kerr-Newman-de Sitter solution. The proof uses the non-linear stability of the Kerr-Newman-de Sitter family of black holes for small angular…
Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning. In this pape…
In this paper angular curvature measures are investigated. Our first result is a complete classification of translation-invariant angular smooth curvature measures on . Subsequently, we use this result to show that the class of angular curvature measures on a Riemannian manifold is preserved by both the p…
Formulae for mass and angular momentum transformations under BMS transformations derived from curvature and metric.
New method resolves ambiguity in measuring black hole merger angular momentum.
Proposes an alternative invariance penalty to address domain generalization issues.
Study proves inequality linking black hole properties and angular momentum.
Researchers prove CWY angular momentum is supertranslation invariant in double null gauge.
The paper defines cross-section continuity for angular momentum definitions and finds the CWY definition valid.
Formulae track evolution of angular momentum and center of mass at null infinity.
In the paper [4] is presented a theory which unifies the gravitation theory and the mechanical effects, which is different from the Riemannian theories like GTR. Moreover it is built in the style of the electomagnetic field theory. This paper is a continuation of [4] such that the complex variant of that theory yields …
New definition of angular momentum avoids supertranslation ambiguity.
Proposes AE for robust PCA, improving robustness to outliers.
For a spacelike 2-surface in spacetime, we propose a new definition of quasi-local angular momentum and quasi-local center of mass, as an element in the dual space of the Lie algebra of the Lorentz group. Together with previous defined quasi-local energy-momentum, this completes the definition of conserved quantities i…
Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization. The embeddings learned by softmax are not discriminative enough for open-set verification tasks. Angular based embedding learning target can achieve such discriminativenes…
Study limits of quasi-local angular momentum at infinity of gravitating systems.
The paper provides bounds for the empirical angular measure and applies them to improve statistical learning in extreme regions.
New memory effect discovered in gravitational wave behavior.
New method for spatiotemporal data regression using Gaussian processes.
Researchers calculate Hofer-Zehnder capacity for twisted tangent bundles over surfaces.
Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e.g. portfolio monitoring, insurance, environmental risk management and anomaly detection. One convenient (non-parametric) characterization of extremal de…
We exam the validity of the definition of the ADM angular momentum without the parity assumption. Explicit examples of asymptotically flat hypersurfaces in the Minkowski spacetime with zero ADM energy-momentum vector and finite non-zero angular momentum vector are presented. We also discuss the Beig-Ó Murchadha-Regge-T…
Paper develops a novel approach to identify clusters of features in multivariate extremes.
Generative models improve angular variable simulation in high dimensions.
We show how to reduce the general formulation of the mass-angular momentum-charge inequality, for axisymmetric initial data of the Einstein-Maxwell equations, to the known maximal case whenever a geometrically motivated system of equations admits a solution. It is also shown that the same reduction argument applies to …
New method uses neural networks for accurate angle estimation in noisy conditions.
We present a general sufficient condition for the formation of black holes due to concentration of angular momentum. This is expressed in the form of a universal inequality, relating the size and angular momentum of bodies, and is proven in the context of axisymmetric initial data sets for the Einstein equations which …
Study reformulates Finsler metrizability problems using geodesic invariance.
AngularGrad optimizes CNNs by considering gradient direction, improving convergence.