Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub*.

Abstract--Underwater environments pose significant challenges for visual Simultaneous Localization and Mapping (SLAM) systems due to limited visibility, inadequate illumination, and sporadic loss of structural features in images. Addressing these challenges, this paper introduces a novel, tightly-coupled Acoustic-Visual-Inertial SLAM approach, termed AQUA-SLAM, to fuse a Doppler Velocity Log (DVL), a stereo camera, and an Inertial Measurement Unit (IMU) within a graph optimization framework. The proposed system will be made open-source for the community. These vehicles are indispensable occasionally outside the camera's field of view leading to for tasks such as seabed mapping, pipeline and intermittent loss of visual tracking. Therefore, although visual cable inspections, biological and environmental monitoring, SLAM techniques have recently made tremendous progress and the maintenance of underwater infrastructure. A key in terrestrial settings [1], [2], [3], their performance and application area is the detailed visual inspection of subsea robustness are inevitably compromised in underwater due to structures, including offshore wind turbine foundations, where the complex and dynamic nature of aquatic environments. Considering cameras are widely equipped on underwater (IMU), known as visual-inertial SLAM (VI-SLAM) [4], [5], robots, visual Simultaneous Localization and Mapping can alleviate some of the challenges arising from transient, (SLAM) techniques emerge as natural solutions. The rapid attenuation of underwater SLAM systems, particularly against shortterm of light energy in water severely limits the visibility of visual disruptions, can be substantially enhanced [6]. However, most of the challenges for underwater vision, such Moreover, underwater vision often suffers from poor lighting as the limited visibility and the "marine snow", are longterm and blizzards of "marine snow" caused by small particles of effects that last at least from tens of seconds to a few organic matter in water, severely reducing image quality with minutes before being mitigated. VI-SLAM also encounters increased motion blur and dynamic image regions.

The proliferation of text-to-image diffusion models has raised significant privacy and security concerns, particularly regarding the generation of copyrighted or harmful images. To address these issues, research on machine unlearning has developed various concept erasure methods, which aim to remove the effect of unwanted data through post-hoc training. However, we show these erasure techniques are vulnerable, where images of supposedly erased concepts can still be generated using adversarially crafted prompts. We introduce RECORD, a coordinate-descent-based algorithm that discovers prompts capable of eliciting the generation of erased content. We demonstrate that RECORD significantly beats the attack success rate of current state-of-the-art attack methods. Furthermore, our findings reveal that models subjected to concept erasure are more susceptible to adversarial attacks than previously anticipated, highlighting the urgency for more robust unlearning approaches. We open source all our code at https://github.com/LucasBeerens/RECORD

Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as the Dirichlet energy. While these metrics are related to oversmoothing, we argue they have critical limitations and fail to reliably capture oversmoothing in realistic scenarios. For instance, they provide meaningful insights only for very deep networks and under somewhat strict conditions on the norm of network weights and feature representations. As an alternative, we propose measuring oversmoothing by examining the numerical or effective rank of the feature representations. We provide theoretical support for this approach, demonstrating that the numerical rank of feature representations converges to one for a broad family of nonlinear activation functions under the assumption of nonnegative trained weights. To the best of our knowledge, this is the first result that proves the occurrence of oversmoothing without assumptions on the boundedness of the weight matrices. Along with the theoretical findings, we provide extensive numerical evaluation across diverse graph architectures. Our results show that rank-based metrics consistently capture oversmoothing, whereas energy-based metrics often fail. Notably, we reveal that a significant drop in the rank aligns closely with performance degradation, even in scenarios where energy metrics remain unchanged.