Tootfinder

This https://arxiv.org/abs/2403.19612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handle…

This https://arxiv.org/abs/2403.19612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handle…

[2024-03-05 Tue (UTC), no new articles found for cs.CV Computer Vision and Pattern Recognition]

Long Short-Term Memory Pattern Recognition in Currency Trading
Jai Pal
https://arxiv.org/abs/2403.18839 https://arxiv.org/pdf/2403.18…

Long Short-Term Memory Pattern Recognition in Currency Trading
This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics and identifying potential trading opportunities. By dissecting the intricacies of these phases, the study sheds light on the creation of…

Something I wrote a few years ago in an explainer of #AI from the from the perspective of democracy, rights and governance.
https://www.civicspace.tech/technologies/machine-lea…

Good stuff.
#nerds #nerdsRule

[2024-04-02 Tue (UTC), no new articles found for cs.CV Computer Vision and Pattern Recognition]

Quantum Annealing Inspired Algorithms for Track Reconstruction at High Energy Colliders
Hideki Okawa, Qing-Guo Zeng, Xian-Zhe Tao, Man-Hong Yung
https://arxiv.org/abs/2402.14718 <…

Quantum Annealing Inspired Algorithms for Track Reconstruction at High Energy Colliders
Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high energy collider physics. It is known for enormous consumption of the computing resources, especially when the particle multiplicity is high. This would indeed be the conditions at future colliders such as the High Luminosity Large Hadron Collider and Super Proton Proton Collider. Track reconstruction can be formulated as a quadratic unconstrained binary optimization (QUBO…

This https://arxiv.org/abs/2305.08597 has been replaced.
initial toot: https://mastoxiv.page/@…

Analytical solution for optimal protocols of weak drivings
One of the main objectives of science is the recognition of a general pattern in a particular phenomenon in some particular regime. In this work, this is achieved with the analytical expression for the optimal protocol that minimizes the thermodynamic work and its variance for finite-time, isothermal, and weak processes. The method that solves the Euler-Lagrange integral equation is quite general and depends only on the time-reversal symmetry of the optimal protocol. The solution is composed of…

High-confidence pseudo-labels for domain adaptation in COVID-19 detection
Robert Turnbull, Simon Mutch
https://arxiv.org/abs/2403.13509 https://

High-confidence pseudo-labels for domain adaptation in COVID-19 detection
This paper outlines our submission for the 4th COV19D competition as part of the `Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis' (DEF-AI-MIA) workshop at the Computer Vision and Pattern Recognition Conference (CVPR). The competition consists of two challenges. The first is to train a classifier to detect the presence of COVID-19 from over one thousand CT scans from the COV19-CT-DB database. The second challenge is to perform domain adaptation by taking the dataset…

This https://arxiv.org/abs/2312.12823 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Detecting Multiple Change Points in Distributional Sequences Derived from Structural Health Monitoring Data: An Application to Bridge Damage Detection
Detecting damage in critical structures using monitored data is a fundamental task of structural health monitoring, which is extremely important for maintaining structures' safety and life-cycle management. Based on statistical pattern recognition paradigm, damage detection can be conducted by assessing changes in the distribution of properly extracted damage-sensitive features (DSFs). This can be naturally formulated as a distributional change-point detection problem. A good change-point detec…

A precise bare simulation approach to the minimization of some distances. II. Further Foundations
Michel Broniatowski, Wolfgang Stummer
https://arxiv.org/abs/2402.08478

A precise bare simulation approach to the minimization of some distances. II. Further Foundations
The constrained minimization (respectively maximization) of directed distances and of related generalized entropies is a fundamental task in information theory as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition. In our previous paper "A precise bare simulation approach to the minimization of some distances. I. Foundations", we obtained such kind of constrained optima by a new dimension-free precise bare (pure) si…

Assessing the similarity of real matrices with arbitrary shape
Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio, Michael Denker, Sonja Gr\"un, Sacha J. van Albada, Markus Diesmann
https://arxiv.org/abs/2403.17687

Assessing the similarity of real matrices with arbitrary shape
Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here, we propose singular angle similarity (SAS), a measure for …

[2024-04-25 Thu (UTC), no new articles found for cs.CV Computer Vision and Pattern Recognition]

This https://arxiv.org/abs/2312.15583 has been replaced.
link: https://scholar.google.com/scholar?q=a

ITEACNet: Inverted Teacher-studEnt seArch Conversation Network
Due to the widespread applications of conversations in human-computer interaction, Conversation Emotion Recognition (CER) has attracted increasing attention from researchers. In real-world scenarios, the emotional states of both participants in a conversation tend to maintain a relatively stable pattern within the local context, and often encountering issues with incomplete data patterns. Focusing on these two key challenges, we propose a novel framework for incomplete multimodal learning in CE…

Assessing the similarity of real matrices with arbitrary shape
Jasper Albers, Anno C. Kurth, Robin Gutzen, Aitor Morales-Gregorio, Michael Denker, Sonja Gr\"un, Sacha J. van Albada, Markus Diesmann
https://arxiv.org/abs/2403.17687

Assessing the similarity of real matrices with arbitrary shape
Assessing the similarity of matrices is valuable for analyzing the extent to which data sets exhibit common features in tasks such as data clustering, dimensionality reduction, pattern recognition, group comparison, and graph analysis. Methods proposed for comparing vectors, such as cosine similarity, can be readily generalized to matrices. However, this approach usually neglects the inherent two-dimensional structure of matrices. Here, we propose singular angle similarity (SAS), a measure for …

This https://arxiv.org/abs/2403.02192 has been replaced.
link: https://scholar.google.com/scholar?q=a

Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference. The Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International Conferences respectively. It includes two Challenges on: i) Covid-19 Dete…

This https://arxiv.org/abs/2401.13666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Algebraic methods for solving recognition problems with non-crossing classes
In this paper, we propose to consider various models of pattern recognition. At the same time, it is proposed to consider models in the form of two operators: a recognizing operator and a decision rule. Algebraic operations are introduced on recognizing operators, and based on the application of these operators, a family of recognizing algorithms is created. An upper estimate is constructed for the model, which guarantees the completeness of the extension.