Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints

• URL: http://arxiv.org/abs/2406.15004v1
• Date: Fri, 21 Jun 2024 09:32:09 GMT
• Title: Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints
• Authors: Benjamin Udofia, Tushar Jogi, Markus Stricker,
• Abstract summary: This study employs Isomap, a manifold learning technique, to unveil the intrinsic structure of high-dimensional density field data of dislocation structures. The resulting maps provide a systematic framework for quantitatively comparing dislocation structures, offering unique fingerprints based on density fields.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for dislocation networks evolving as a consequence of plastic deformation of crystalline systems. Our study employs Isomap, a manifold learning technique, to unveil the intrinsic structure of high-dimensional density field data of dislocation structures from different compression axis. The resulting maps provide a systematic framework for quantitatively comparing dislocation structures, offering unique fingerprints based on density fields. Our novel, unbiased approach contributes to the quantitative classification of dislocation structures which can be systematically extended.

Related papers

• Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework [14.899157568336731]
  DeepBayesic is a novel framework that integrates Bayesian principles with deep neural networks to model the underlying distributions. We evaluate our approach on several mobility datasets, demonstrating significant improvements over state-of-the-art anomaly detection methods.
  arXiv  Detail & Related papers  (2024-10-01T19:02:06Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic Topology [9.537910170141467]
  We study how the topology of feature embedding space changes as it passes through the layers of a well-trained deep neural network (DNN) through Betti numbers. We demonstrate that as depth increases, a topologically complicated dataset is transformed into a simple one, resulting in Betti numbers attaining their lowest possible value.
  arXiv  Detail & Related papers  (2023-11-08T10:45:12Z)
• Data-Free Learning of Reduced-Order Kinematics [54.85157881323157]
  We produce a low-dimensional map whose image parameterizes a diverse yet low-energy submanifold of configurations. We represent subspaces as neural networks that map a low-dimensional latent vector to the full configuration space. This formulation is effective across a very general range of physical systems.
  arXiv  Detail & Related papers  (2023-05-05T20:53:36Z)
• NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems [9.77270939559057]
  This paper explores a new framework, dubbed NeuralSI, for structural identification. Our approach seeks to estimate nonlinear parameters from governing equations. The trained model can also be extrapolated under both standard and extreme conditions.
  arXiv  Detail & Related papers  (2022-08-26T16:32:51Z)
• Intrinsic dimension estimation for discrete metrics [65.5438227932088]
  In this letter we introduce an algorithm to infer the intrinsic dimension (ID) of datasets embedded in discrete spaces. We demonstrate its accuracy on benchmark datasets, and we apply it to analyze a metagenomic dataset for species fingerprinting. This suggests that evolutive pressure acts on a low-dimensional manifold despite the high-dimensionality of sequences' space.
  arXiv  Detail & Related papers  (2022-07-20T06:38:36Z)
• Diffusion Mechanism in Residual Neural Network: Theory and Applications [12.573746641284849]
  In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points. We propose a novel diffusion residual network (Diff-ResNet) internally introduces diffusion into the architectures of neural networks. Under the structured data assumption, it is proved that the proposed diffusion block can increase the distance-diameter ratio that improves the separability of inter-class points.
  arXiv  Detail & Related papers  (2021-05-07T10:42:59Z)
• Unveiling the Potential of Structure-Preserving for Weakly Supervised Object Localization [71.79436685992128]
  We propose a two-stage approach, termed structure-preserving activation (SPA), towards fully leveraging the structure information incorporated in convolutional features for WSOL. In the first stage, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network. In the second stage, we propose a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps.
  arXiv  Detail & Related papers  (2021-03-08T03:04:14Z)
• A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies [0.0]
  Deep learning is a valid way for data exploration in geomorphology and related fields. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results.
  arXiv  Detail & Related papers  (2021-03-06T14:05:38Z)
• Understanding and Diagnosing Vulnerability under Adversarial Attacks [62.661498155101654]
  Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. We propose a novel interpretability method, InterpretGAN, to generate explanations for features used for classification in latent variables. We also design the first diagnostic method to quantify the vulnerability contributed by each layer.
  arXiv  Detail & Related papers  (2020-07-17T01:56:28Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.