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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification 18

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Wang, Rui Shen, Chuanfu Marin-Jimenez, Manuel J. Huang, George Q. Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong University of Cordoba Department of Computing and Numerical Analysis Spain The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798350364132

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition. © 2024 IEEE.

关键词： Unattended sensors

Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure 15

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Barycentre Averaging for the Move-Split-Merge Time Series Di...

15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Holder, Christopher Guijo-Rubio, David Bagnall, Anthony School of Computing Sciences University of East Anglia Norwich United Kingdom Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9789897586712

Distance functions play a core role in many time series machine learning algorithms for tasks such as clustering, classification and regression. Time series often require bespoke distance functions because small offsets in time can lead to large distances between series that are conceptually similar. Elastic distances compensate for misalignment by creating a path through a cost matrix by warping and/or editing time series. Time series are most commonly clustered with partitional algorithms such as k-means and k-medoids using elastic distance measures such as Dynamic Time Warping (DTW). The distance is used to assign cases to the closest cluster representative. k-means requires the averaging of time series to find these representative centroids. If DTW is used to assign membership, but the arithmetic mean is used to find centroids, k-means performance degrades significantly. An averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), overcomes the averaging problem however, can only be used with DTW. As such alternative distance functions such as Move-Split-Merge (MSM) are forced to use the arithmetic mean to compute new centroids and suffer similar degraded performance as k-means-DTW without DBA. To address this we propose a averaging method for MSM distance, MSM Barycentre Averaging (MBA) and show that when used to find centroids it significantly improves MSM based k-means and is better than commonly used alternatives. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Time series

Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners

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arXiv 2024年

作者： Luna, J.M. Fardoun, H.M. Padillo, F. Romero, C. Ventura, S. Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope with extremely large datasets. As an additional feature, the proposal includes a threshold value to denote the number of courses that each discovered rule should satisfy. A post-processing step is also included so redundant subgroups can be removed. The experimental stage is carried out by considering de-identified data from the first year of 16 MITx and HarvardX courses on the edX platform. Experimental results demonstrate that the proposed MapReduce approach outperforms traditional sequential subgroup discovery approaches, achieving a runtime that is almost constant for different courses. Additionally, thanks to the final post-processing step, only interesting and not-redundant rules are discovered, hence reducing the number of subgroups in one or two orders of magnitude. Finally, the discovered subgroups are easily used by courses’ instructors not only for descriptive purposes but also for additional tasks such as recommendation or personalization. Copyright © 2024, The Authors. All rights reserved.

关键词： MapReduce

An energy stable and conservative multiplicative dynamical low-rank discretization for the Su-Olson problem

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arXiv 2025年

作者： Baumann, Lena Einkemmer, Lukas Klingenberg, Christian Kusch, Jonas University of Wuerzburg Department of Mathematics Wuerzburg Germany University of Innsbruck Numerical Analysis and Scientific Computing Innsbruck Austria Norwegian University of Life Sciences Department of Data Science Ås Norway

computing numerical solutions of the thermal radiative transfer equations on a finely resolved grid can be costly due to high computational and memory requirements. A numerical reduced order method that has recently been applied to a wide variety of kinetic partial differential equations is the concept of dynamical low-rank approximation (DLRA). In this paper, we consider the thermal radiative transfer equations with Su-Olson closure, leading to a linearized kinetic model. For the conducted theoretical and practical considerations we use a multiplicative splitting of the distribution function that poses additional challenges in finding an energy stable discretization and deriving a hyperbolic Courant-Friedrichs-Lewy (CFL) condition. We propose such an energy stable DLRA scheme that makes use of the augmented basis update & Galerkin integrator. This integrator allows for additional basis augmentations, enabling us to give a mathematically rigorous proof of energy stability and local mass conservation. numerical examples confirm the derived properties and show the computational advantages of the DLRA scheme compared to a numerical solution of the full system of *** Codes 35L65, 35Q49, 65M12, 65M22 © 2025, CC BY-NC-ND.

关键词： Radiative transfer

Hexagons all the way down: grid cells as a conformal isometric map of space

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PLoS Computational Biology 2025年第2期21卷 e1012804页

作者： Schøyen, Vemund Sigmundson Beshkov, Kosio Pettersen, Markus Borud Hermansen, Erik Holzhausen, Konstantin Malthe-Sørenssen, Anders Fyhn, Marianne Lepperød, Mikkel Elle Department of Biosciences University of Oslo Oslo Norway Department of Physics University of Oslo Oslo Norway Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway Department of Numerical Analysis and Scientific Computing Simula Research Laboratory AS Oslo Norway

Grid cells in the entorhinal cortex are known for their hexagonal spatial activity patterns and are thought to provide a neural metric for space, and support path integration. In this study, we further investigate grid cells as a metric of space by optimising them for a conformal isometric (CI) map of space using a model based on a superposition of plane waves. By optimising the phases within a single grid cell module, we find that the module can form a CI of two-dimensional flat space with phases arranging into a regular hexagonal pattern, supporting an accurate spatial metric. Additionally, we find that experimentally recorded grid cells exhibit CI properties, with one example module showing a phase arrangement similar to the hexagonal pattern observed in our model. These findings provide computational and preliminary experimental support for grid cells as a CI-based spatial representation. We also examine other properties that emerge in CI-optimised modules, including consistent energy expenditure across space and the minimal cell count required to support unique representation of space and maximally topologically persistent toroidal population activity. Altogether, our results suggest that grid cells are well-suited to form a CI map, with several beneficial properties arising from this organisation. © 2025 Schøyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Cytology

Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

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arXiv 2024年

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

关键词： Generative adversarial networks

A stable multiplicative dynamical low-rank discretization for the linear Boltzmann-BGK equation

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arXiv 2024年

The numerical method of dynamical low-rank approximation (DLRA) has recently been applied to various kinetic equations showing a significant reduction of the computational effort. In this paper, we apply this concept to the linear Boltzmann-Bhatnagar-Gross-Krook (Boltzmann-BGK) equation which due its high dimensionality is challenging to solve. Inspired by the special structure of the non-linear Boltzmann-BGK problem, we consider a multiplicative splitting of the distribution function. We propose a rank-adaptive DLRA scheme making use of the basis update & Galerkin integrator and combine it with an additional basis augmentation to ensure numerical stability, for which an analytical proof is given and a classical hyperbolic Courant–Friedrichs–Lewy (CFL) condition is derived. This allows for a further acceleration of computational times and a better understanding of the underlying problem in finding a suitable discretization of the system. numerical results of a series of different test examples confirm the accuracy and efficiency of the proposed method compared to the numerical solution of the full *** Codes 35Q20, 65L04, 65M12, 65M22 © 2024, CC BY-NC-ND.

关键词： Boltzmann equation

CUT FINITE ELEMENT DISCRETIZATIONS OF CELL-BY-CELL EMI ELECTROPHYSIOLOGY MODELS

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arXiv 2023年

作者： Berre, Nanna Rognes, Marie E. Massing, André Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway Department for Numerical Analysis and Scientific Computing Simula Research Laboratory Oslo Norway

The EMI (Extracellular-Membrane-Intracellular) model describes electrical activity in excitable tissue, where the extracellular and intracellular spaces and cellular membrane are explicitly represented. The model couples a system of partial differential equations in the intracellular and extracellular spaces with a system of ordinary differential equations on the membrane. A key challenge for the EMI model is the generation of high-quality meshes conforming to the complex geometries of brain cells. To overcome this challenge, we propose a novel cut finite element method (CutFEM) where the membrane geometry can be represented independently of a structured and easy-to-generated background mesh for the remaining computational domain. Starting from a Godunov splitting scheme, the EMI model is split into separate PDE and ODE parts. The resulting PDE part is a non-standard elliptic interface problem, for which we devise two different CutFEM formulations: one single-dimensional formulation with the intra/extracellular electrical potentials as unknowns, and a multi-dimensional formulation that also introduces the electrical current over the membrane as an additional unknown leading to a penalized saddle point problem. Both formulations are augmented by suitably designed ghost penalties to ensure stability and convergence properties that are insensitive to how the membrane surface mesh cuts the background mesh. For the ODE part, we introduce a new unfitted discretization to solve the membrane bound ODEs on a membrane interface that is not aligned with the background mesh. Finally, we perform extensive numerical experiments to demonstrate that CutFEM is a promising approach to efficiently simulate electrical activity in geometrically resolved brain *** Codes 65M60, 65M85, 65N30, 65N85, 92C20 Copyright © 2023, The Authors. All rights reserved.

关键词： Electrophysiology

DREAMS: A python framework to train deep learning models with model card reporting for medical and health applications

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arXiv 2024年

作者： Khadka, Rabindra Lind, Pedro G. Yazidi, Anis Belhadi, Asma Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing OsloN-0164 Norway

Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meaningful patterns, leading to valuable insights for both clinical and research purposes. However, most of the frameworks so far, designed for EEG data analysis, are either too focused on pre-processing or in deep learning methods per, making their use for both clinician and developer communities problematic. Moreover, critical issues such as ethical considerations, biases, uncertainties, and the limitations inherent in AI models for EEG data analysis are frequently overlooked, posing challenges to the responsible implementation of these technologies. In this paper, we introduce a comprehensive deep learning framework tailored for EEG data processing, model training and report generation. While constructed in way to be adapted and developed further by AI developers, it enables to report, through model cards, the outcome and specific information of use for both developers and clinicians. In this way, we discuss how this framework can, in the future, provide clinical researchers and developers with the tools needed to create transparent and accountable AI models for EEG data analysis and diagnosis. © 2024, CC BY.

关键词： Electroencephalography