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.
New method detects rock type changes in real-time during drilling.
Machine learning predicts rock properties from routine core analysis.
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicitly determine the type of the drilled rock are located farther than 15m from the drilling bit. As a result, the target area runaways can be d…
Deep learning model improves seismic rock property estimation.
ROCK method generalizes MOCK for learning dynamical systems efficiently.
Momentum speeds up evolutionary processes in machine learning.
Automated rock fragmentation assessment using deep learning and spatial statistics.
A machine learning method predicts rock permeability from 3D images.
Paper introduces impact curves for evaluating binarized regression models with varying costs.
Deep learning boosts micro-CT image resolution and texture recovery.
With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoi…
AI beats 95% of humans in Rock-Paper-Scissors.
Machine learning identifies types of alterations in historical manuscripts.
DeepFlow uses deep generative models to solve history matching problems.
Dual neural networks tackle uncertainty in geophysical data.
Improved Bayesian computation for imaging problems using a new MCMC method.
New method alters Seifert surfaces without compression.
Study examines how digital image alterations affect AI classification models.
Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of variou…
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…
Develops a Bayesian non-parametric approach for signal separation with varying components.
Study examines AI's role in robo-investing, focusing on benefits for specific investors.
New framework for robust uncertainty quantification in strategic settings.
Framework identifies brain connectivity alterations for MDD patients using limited rs-fMRI data.
The exposition has been significantly altered, hopefully improved.
In the present work, we study the decompositions of codimension-one transitions that alter the singular set the of stable maps of into the topological behaviour of the singular set and the singularities in the branch set that involves cuspidal curves and swallowtails that alter the singular set. W…
New algorithm detects tensor dependence structure alterations efficiently.
Dataset for rainfall modeling in central Europe from 1981-2011.
It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack --- and learnability by a noise-free process that mixes training examples, the Crossover Process (cp). One key point is that the cp~is typicall…
We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels. Formulated as a semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and labeling approach enables the use of a rich set of segment-level features, such as segment…
Radiomics identifies subtle cardiac changes in hypertension.
We consider a simple stochastic differential equation for modeling bubbles in social context. A prime example is bubbles in asset pricing, but similar mechanisms may control a range of social phenomena driven by psychological factors (for example, popularity of rock groups, or a number of students pursuing a given majo…
A simple method to create new 4-manifolds by altering fundamental groups.
We pursue the analogy of a framed flow category with the flow data of a Morse function. In classical Morse theory, Morse functions can sometimes be locally altered and simplified by the Morse moves. These moves include the Whitney trick which removes two oppositely framed flowlines between critical points of adjacent i…
The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have si…
Bayesian model detects altered neural circuits in MCI patients.
A new mathematical approach detects frequency-based alterations in brain networks.
New method explains visual models by altering features causally.
Framed flow categories were introduced by Cohen-Jones-Segal as a way of encoding the flow data associated to a Floer functional. A framed flow category gives rise to a CW-complex with one cell for each object of the category. The idea is that the Floer invariant should take the form of the stable homotopy type of the r…
Graph neural networks improve with edge similarity constraints in RNA structure analysis.
(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…
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
Can one reduce the size of a graph without significantly altering its basic properties? The graph reduction problem is hereby approached from the perspective of restricted spectral approximation, a modification of the spectral similarity measure used for graph sparsification. This choice is motivated by the observation…
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can ex…
New hierarchical RL agent learns to generalize in complex multi-agent games.
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we a…