ABSTRACT Robust place recognition is essential for reliable localization in robotics, particularly in complex environments with frequent indoor–outdoor transitions. However, existing LiDAR‐based datasets often focus on outdoor scenarios and lack seamless domain shifts. In this paper, we propose RoboLoc, a benchmark dataset designed for GPS‐free place recognition in indoor–outdoor environments with floor transitions. RoboLoc features real‐world robot trajectories, diverse elevation profiles, and transitions between structured indoor and unstructured outdoor domains. We benchmark a variety of state‐of‐the‐art models, point‐based, voxel‐based, and BEV‐based architectures, highlighting their generalizability domain shifts. RoboLoc provides a realistic testbed for developing multi‐domain localization systems in robotics and autonomous navigation.

Electroencephalogram (EEG)-based brain-computer interfaces face challenges in data insufficiency to train neural networks for tasks and generalizability for multiple subjects. To address the issue of data scarcity in EEG research, transfer learning using EEG foundation models (EFMs) has recently gained attention for its ability to leverage prior knowledge. Although transfer learning with EFMs enables tasks to be performed with limited training data, their increasing size presents significant computational challenges. Parameter-efficient transfer learning (PETL) methods address this computational issue by tuning only a small subset of parameters from the pre-trained model. However, existing PETL methods mostly fail to account for the high-dimensional nature of EEG data, which limits their ability to fully leverage the prior knowledge of the EFM when applied to downstream tasks. To address these challenges, we propose a novel PETL method with a TASk-relevanT fEature Focusing modULe (TASTEFUL) to transfer EFMs efficiently. TASTEFUL is designed to focus on task-relevant features and efficiently learn representations tailored for downstream tasks. We evaluated our proposed TASTEFUL on tasks using publicly available EEG datasets, demonstrating its superior performance. Finally, our work highlights TASTEFUL's potential to enhance the practical application of EFMs, marking a significant advancement in PETL for EFMs.

Time series data, widely used in fields such as climate studies, finance, and healthcare, often face scarcity in rare scenarios and privacy concerns, prompting growing interest in time series synthesis. Diffusion models have shown strong potential for generating high-quality data, but challenges remain in capturing long-range dependencies and complex patterns. We propose a novel diffusion model that integrates time-domain information with rich frequency-domain features, accounting for differences in noise decay rates across frequencies. Instead of arbitrary frequency splits used in prior works, we partition components based on spectral density, model them separately within the denoising backbone, and fuse them with time-domain features. This enables effective capture of both global and local patterns, enhancing representation of high- and low-frequency information. Extensive experiments on multiple public datasets show promising performance, and analyses including long-term generation and ablation studies demonstrate the model's ability to learn and represent complex time series distributions.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: 1) temporal variability; 2) incomplete observations; and 3) temporal geometric characteristics. However, many pioneer deep learning-based approaches addressing data variability and sparsity have yet to consider inherent geometrical properties sufficiently. These properties are integral to modeling as they correlate with brain region size, thickness, volume, and shape in AD progression. The ordinary differential equation-based geometric modeling method (ODE-RGRU) has recently emerged as a promising strategy for modeling time-series data by intertwining a recurrent neural network (RNN) and an ODE in Riemannian space. Despite its achievements, ODE-RGRU encounters limitations when extrapolating positive definite symmetric matrices from incomplete samples, leading to feature reverse occurrences that are particularly problematic, especially within the clinical facet. Therefore, this study proposes a novel geometric learning approach that models longitudinal MRI biomarkers and cognitive scores by combining three modules: topological space shift, ODE-RGRU, and trajectory estimation. We have also developed a training algorithm that integrates the manifold mapping with monotonicity constraints to reflect measurement transition irreversibility. We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings. Furthermore, we thoroughly analyze our proposed framework through an ablation study.

Modeling non-euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive definite matrix is being actively studied in computer vision, signal processing, and medical image analysis, due to its ability to learn beneficial statistical representations. However, owing to its rigid constraints, it remains challenging to optimization problems and inefficient computational costs, especially, when incorporating it with a deep learning framework. In this paper, we propose a framework to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space, by which it becomes feasible not only to efficiently solve optimization problems but also to greatly reduce computation costs. Further, for dynamic modeling of time-series data, we devise a continuous manifold learning method by systematically integrating a manifold ordinary differential equation and a gated recurrent neural network. It is worth noting that due to the nice parameterization of matrices in a Cholesky space, training our proposed network equipped with Riemannian geometric metrics is straightforward. We demonstrate through experiments over regular and irregular time-series datasets that our proposed model can be efficiently and reliably trained and outperforms existing manifold methods and state-of-the-art methods in various time-series tasks.

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented self-supervised learning methods and a novel deep architecture to learn rich representation, including information about the diverse spectral characteristics of neural oscillations, the spatial properties of electrode sensor distribution, and the temporal patterns of both the global and local viewpoints. Along with the proposed self-supervision strategies and deep architectures, we devise a feature normalization strategy to resolve the intra-/inter-subject variability problem. We demonstrate the validity of our proposed deep learning framework on the four publicly available datasets by conducting comparisons with the state of the art baselines. It is also noteworthy that we exploit the same network architecture for the four different EEG paradigms and outperform the comparison methods, thereby meeting the challenge of the task-dependent network architecture engineering in EEG-based applications.

Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer, a novel transfer learning framework that effectively models 3-D volumetric images as a sequence of 2-D image slices. To improve the high-level representation in 3-D-form empowering spatial relations, we use a multiview approach that leverages information from three planes of the 3-D volume, while providing parameter-efficient training. For building a source model generally applicable to various tasks, we pretrain the model using self-supervised learning (SSL) for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset. Our pretrained model is evaluated on three downstream tasks: 1) brain disease diagnosis; 2) brain age prediction; and 3) brain tumor segmentation, which are widely studied in brain MRI research. Experimental results demonstrate that our Medical Transformer outperforms the state-of-the-art (SOTA) transfer learning methods, efficiently reducing the number of parameters by up to approximately 92% for classification and regression tasks and 97% for segmentation task, and it also achieves good performance in scenarios where only partial training samples are used.

Alzheimer’s disease (AD) is widely aware as a neurodegenerative disease that is characterized as a leading cause of irreversible progressive dementia. From a clinical perspective, it is vital to forecast a patient’s progression over time. In that regard, there have been rigorous researches on AD progression modeling with structural magnetic resonance imaging (MRI). Methodologically, there are three major aspects of MRI modeling: (i) variability over time, (ii) sparseness in observations, and (iii) geometrical properties in temporal dynamics. While the existing deep-learning-based methods have addressed variability or sparsity in data, there is still a need to take into account the inherent geometrical properties. Recently, geometric modeling based on ordinary differential equations (ODE-RGRU) has shown its ability in various time-series data by combining an RNN and an ordinary differential equation (ODE) in symmetric positive definite (SPD) space. Despite the success of ODE-RGRU in time-series data modeling, it is limited to estimating the SPD matrix from sparse data with missing values. To this end, we propose a novel geometric learning framework for AD progression modeling to tackle the aforementioned issues simultaneously. And, we also propose training algorithms for manifold mapping on irregular and incomplete MRI and cognitive scores observations. Our proposed framework efficiently learns three major aspects of longitudinal MRI biomarker and cognitive scores by the manifold transformation module, ODE-RGRU, and missing value estimation module. We demonstrate the effectiveness of our method in experiments that forecast multi-class classification and cognitive scores over time. Additionally, we provide a multi-faceted analysis of the proposed method through an ablation study.

While compliance control scheme is done for the wearable robot combined with the linear actuator and sensor on it as intended by the user, oscillations occur due to the joint kinematical mechanism. In this paper, the cause of oscillation on the knee joint is analyzed in detail. Also, it is shown that the moment arm is varied with joint angle, it is confirmed that sensed pressure is changed by the variation. In addition, for canceling the oscillation, torque sensor - using the method to move its force/pressure sensor position from the cylinder to the joint, and a wire-pulley system that adjusts the moment arm with a mechanical impedance compensation are proposed. Finally, simulations and experiments are done for demonstrating oscillation reduction by using the DSME wearable robot.