However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in expectation over the observation space.

Data quality is critical to Intelligent Transportation Systems (ITS), as complete and accurate traffic data underpin reliable decision-making in traffic control and management. Recent advances in low-rank tensor recovery algorithms have shown strong potential in capturing the inherent structure of high-dimensional traffic data and restoring degraded observations. However, traditional batch-based methods demand substantial computational and storage resources, which limits their scalability in the face of continuously expanding traffic data volumes. Moreover, recent online tensor recovery methods often suffer from severe performance degradation in complex real-world scenarios due to their insufficient exploitation of the intrinsic structural properties of traffic data. To address these challenges, we reformulate the traffic data recovery problem within a streaming framework, and propose a novel online robust tensor recovery algorithm that simultaneously leverages both the global spatio-temporal correlations and local consistency of traffic data, achieving high recovery accuracy and significantly improved computational efficiency in large-scale scenarios. Our method is capable of simultaneously handling missing and anomalous values in traffic data, and demonstrates strong adaptability across diverse missing patterns. Experimental results on three real-world traffic datasets demonstrate that the proposed approach achieves high recovery accuracy while significantly improving computational efficiency by up to three orders of magnitude compared to state-of-the-art batch-based methods. These findings highlight the potential of the proposed approach as a scalable and effective solution for traffic data quality enhancement in ITS.

However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in expectation over the observation space.

First, an OT -based partial alignment with adaptive filling is designed to detect common classes without any predefined threshold values for realistic UniDA.

In real-world scenarios, spatiotemporal traffic data frequently experiences dual degradation from missing values and noise caused by sensor malfunctions and communication failures. Therefore, effective data recovery methods are essential to ensure the reliability of downstream data-driven applications. while classical tensor completion methods have been widely adopted, they are incapable of modeling noise, making them unsuitable for complex scenarios involving simultaneous data missingness and noise interference. Existing Robust Tensor Completion (RTC) approaches offer potential solutions by separately modeling the actual tensor data and noise. However, their effectiveness is often constrained by the over-relaxation of convex rank surrogates and the suboptimal utilization of local consistency, leading to inadequate model accuracy. To address these limitations, we first introduce the tensor L1-L2 norm, a novel non-convex tensor rank surrogate that functions as an effective low-rank representation tool. Leveraging an advanced feature fusion strategy, we further develop the gradient tensor L1-L2 norm by incorporating the tensor L1-L2 norm in the gradient domain. By integrating the gradient tensor nuclear L1-L2 norm into the RTC framework, we propose the Robust Tensor Completion via Gradient Tensor Nuclear L1-L2 Norm (RTC-GTNLN) model, which not only fully exploits both global low-rankness and local consistency without trade-off parameter, but also effectively handles the dual degradation challenges of missing data and noise in traffic data. Extensive experiments conducted on multiple real-world traffic datasets demonstrate that the RTC-GTNLN model consistently outperforms existing state-of-the-art methods in complex recovery scenarios involving simultaneous missing values and noise.

Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluation frameworks. We present MapEval, an open-source framework for comprehensive quality assessment of point cloud maps, specifically addressing SLAM scenarios where ground truth map is inherently sparse compared to the mapped environment. Through systematic analysis of existing evaluation metrics in SLAM applications, we identify their fundamental limitations and establish clear guidelines for consistent map quality assessment. Building upon these insights, we propose a novel Gaussian-approximated Wasserstein distance in voxelized space, enabling two complementary metrics under the same error standard: Voxelized Average Wasserstein Distance (AWD) for global geometric accuracy and Spatial Consistency Score (SCS) for local consistency evaluation. This theoretical foundation leads to significant improvements in both robustness against noise and computational efficiency compared to conventional metrics. Extensive experiments on both simulated and real-world datasets demonstrate that MapEval achieves at least \SI{100}{}-\SI{500}{} times faster while maintaining evaluation integrity. The MapEval library\footnote{\texttt{https://github.com/JokerJohn/Cloud\_Map\_Evaluation}} will be publicly available to promote standardized map evaluation practices in the robotics community.

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering.

Recent advancements in monocular neural depth estimation, particularly those achieved by the UniDepth network, have prompted the investigation of integrating UniDepth within a Gaussian splatting framework for monocular SLAM.This study presents UDGS-SLAM, a novel approach that eliminates the necessity of RGB-D sensors for depth estimation within Gaussian splatting framework. UDGS-SLAM employs statistical filtering to ensure local consistency of the estimated depth and jointly optimizes camera trajectory and Gaussian scene representation parameters. The proposed method achieves high-fidelity rendered images and low ATERMSE of the camera trajectory. The performance of UDGS-SLAM is rigorously evaluated using the TUM RGB-D dataset and benchmarked against several baseline methods, demonstrating superior performance across various scenarios. Additionally, an ablation study is conducted to validate design choices and investigate the impact of different network backbone encoders on system performance.

Synthetic data is emerging as a promising way to harness the value of data, while reducing privacy risks. The potential of synthetic data is not limited to privacy-friendly data release, but also includes complementing real data in use-cases such as training machine learning algorithms that are more fair and robust to distribution shifts etc. There is a lot of interest in algorithmic advances in synthetic data generation for providing better privacy and statistical guarantees and for its better utilisation in machine learning pipelines. However, for responsible and trustworthy synthetic data generation, it is not sufficient to focus only on these algorithmic aspects and instead, a holistic view of the synthetic data generation pipeline must be considered. We build a novel system that allows the contributors of real data to autonomously participate in differentially private synthetic data generation without relying on a trusted centre. Our modular, general and scalable solution is based on three building blocks namely: Solid (Social Linked Data), MPC (Secure Multi-Party Computation) and Trusted Execution Environments (TEEs). Solid is a specification that lets people store their data securely in decentralised data stores called Pods and control access to their data. MPC refers to the set of cryptographic methods for different parties to jointly compute a function over their inputs while keeping those inputs private. TEEs such as Intel SGX rely on hardware based features for confidentiality and integrity of code and data. We show how these three technologies can be effectively used to address various challenges in responsible and trustworthy synthetic data generation by ensuring: 1) contributor autonomy, 2) decentralisation, 3) privacy and 4) scalability. We support our claims with rigorous empirical results on simulated and real datasets and different synthetic data generation algorithms.