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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.

168,878 papers · 148 categories

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6121723 · May 202019922001200920172026
48 results for digital rock

Deep learning model improves seismic rock property estimation.

problem Estimating reservoir rock properties from seismic reflection data.
method Proposes a deep learning-based seismic inversion workflow that models seismic traces spatiotemporally.
result Achieves best performance on SEAM dataset with r2r^{2} coefficient of 79.77\%

Automated rock fragmentation assessment using deep learning and spatial statistics.

problem Assessing post-blast rock fragmentation in real-time.
method Fine-tuned YOLO12l-seg model for instance segmentation, followed by spatial statistics.
result Framework accurately assesses rock fragmentation patterns in real-time.

A machine learning method predicts rock permeability from 3D images.

problem Efficiently predict permeability of heterogeneous rocks for planetary and robotic applications.
method Machine learning guided 3D properties recognition of rock morphology from 3D micro CT and MRI images.
result The morphology decoder method accurately predicts permeability from 3D images.

Paper introduces impact curves for evaluating binarized regression models with varying costs.

problem Evaluating binarized regression models with varying costs and instance-specific utility.
method Proposes impact curves to optimize binary decisions across different utilities.
result Impact curves identify conditions where one model is favored over another and quantify model improvement.

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…

2009-10-28abs ↗pdf ↗

Dual neural networks tackle uncertainty in geophysical data.

problem Quantifying and separating epistemic and aleatoric uncertainties in geophysical data.
method Combination of Bayesian Neural Network (BNN) and Artificial Neural Network (ANN).
result Reduces uncertainties in rock and fluid property estimation for better reservoir optimization.

Improved Bayesian computation for imaging problems using a new MCMC method.

problem Challenges in Bayesian computation for imaging inverse problems due to high dimensionality and non-smoothness.
method Introduces a new accelerated proximal MCMC method (ls SK-ROCK) that combines data augmentation and relaxation with proximal MCMC.
result The method converges faster and achieves better accuracy than state-of-the-art approaches.

The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of the earth's subsurface. The model calibration task, commonly referred to as "history matching", can be formalised as an ill-posed inverse pr…

2019-05-14abs ↗pdf ↗

In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box…

2015-07-16abs ↗pdf ↗

In this paper, we show how to construct graph theoretical models of n-dimensional continuous objects and manifolds. These models retain topological properties of their continuous counterparts. An LCL collection of n-cells in Euclidean space is introduced and investigated. If an LCL collection of n-cells is a cover of a…

2017-05-02abs ↗pdf ↗

Incorrect fixed point assertions in digital topology are discussed.

problem Incorrect, incorrectly proven, or trivial fixed point assertions in digital topology.
method Continues earlier work on identifying and critiquing bad fixed point assertions.
result Clarifies the nature and extent of incorrect fixed point assertions in digital topology.

A new method streamlines digital payment programming using smart contracts.

problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.

New framework explains leading digit patterns without probabilistic assumptions.

problem Explaining leading digit distributions without relying on probabilistic models.
method Shift-invariant functional equation and affine-plus-periodic formulas.
result Unified mathematical foundation for understanding digit distributions.

Study shows 'Belt and Road' node cities boost digital finance in China.

problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.