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.
Hierarchical CNNs improve diagnosis of GI diseases from histopathological images.
The paper uses GIS data to predict urban sprawl.
Study improves machine learning models for GI tract disease detection using comprehensive evaluations and cross-dataset testing.
A simple GI loss improves temporal generalization without complex methods.
Using a method introduced by R. Bamler to study the behavior of scalar curvature under continuous deformations of Riemannian metrics, we prove that if a sequence of smooth Riemannian metrics gi on a fixed compact manifold M has isotropic curvature bounded from below by a nonnegative function u, and if gi converge in C …
An online learning framework optimizes pricing and capacity in service systems.
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In th…
Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the…
Can we identify node labels from graph labels?
A killer of a group Gis an element that normally generates G. We show that the group of a cable knot contains infinitely many killers such that no two lie in the same automorphic orbit.
Researchers correct earlier work on surgeries of Gieseking's hyperbolic simplex manifold.
Study on queues with Hawkes arrivals, proving steady-state behavior and developing an efficient algorithm.
This research enhances ML models using gradient information from neural networks.
Revises Schwarzschild manifold rigidity proof for spin manifolds.
This work is a further study on the Generalized Constraint Neural Network (GCNN) model [1], [2]. Two challenges are encountered in the study, that is, to embed any type of prior information and to select its imposing schemes. The work focuses on the second challenge and studies a new constraint imposing scheme for equa…
SINF models transform arbitrary PDFs to target PDFs using 1D slices.
Let GI denote the space of all generic immersions of a surface F into a 3-manifold M. Let q(H_t) denote the number mod 2 of quadruple points of a generic regular homotopy H_t : F -> M. We are interested in defining an invariant Q : GI -> Z/2 such that q(H_t) = Q(H_0) - Q(H_1) for any generic regular homotopy H_t : F ->…
Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise …
Purpose - Functional bowel diseases, including irritable bowel syndrome, chronic constipation, and chronic diarrhea, are some of the most common diseases seen in clinical practice. Many patients describe a range of triggers for altered bowel consistency and symptoms. However, characterization of the relationship betwee…
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
Elite ONNs learn better with synaptic plasticity, improving performance over CNNs.
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method …
A scalable Bayesian linear regression framework for spatial data.
Smooth resolutions found for quotient of R^2 by infinite discrete groups.
We prove the analyticity in time for solutions of two parabolic equations in the whole space, without any decaying or vanishing conditions. One of them involves solutions to the heat equation of exponential growth of order on $\M$. Here $\M$ is or a complete noncompact manifold with Ricci curvature bounded f…
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\…
Solves multi-class imbalanced data problem with geometry-based sampling and synthetic data.
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes…
Unsupervised text encoding models have recently fueled substantial progress in NLP. The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks. We see a striking…
Self-ONNs adapt nodal operators during training for higher diversity and efficiency.