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The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly Non-Local Thermodynamic Equilibrium (NLTE) model with machine learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal component spaces compared to conventional transformations., Comment: 10 pages, 7 figures

Let $D$ be a digraph of order $n$ with adjacency matrix $A(D)$. For $\alpha\in[0,1)$, the $A_{\alpha}$ matrix of $D$ is defined as $A_{\alpha}(D)=\alpha {\Delta}^{+}(D)+(1-\alpha)A(D)$, where ${\Delta}^{+}(D)=\mbox{diag}~(d_1^{+},d_2^{+},\dots,d_n^{+})$ is the diagonal matrix of vertex outdegrees of $D$. Let $\sigma_{1\alpha}(D),\sigma_{2\alpha}(D),\dots,\sigma_{n\alpha}(D)$ be the singular values of $A_{\alpha}(D)$. Then the trace norm of $A_{\alpha}(D)$, which we call $\alpha$ trace norm of $D$, is defined as $\|A_{\alpha}(D)\|_*=\sum_{i=1}^{n}\sigma_{i\alpha}(D)$. In this paper, we study the variation in $\alpha$ trace norm of a digraph when a vertex or an arc is deleted. As an application of these results, we characterize oriented trees and unicyclic digraphs with maximum $\alpha$ trace norm.

Finite state machines (FSMs) regulate sequential circuits, including access to sensitive information and privileged CPU states. Courtesy of contemporary research on laser attacks, laser-based fault injection (LFI) is becoming even more precise where an adversary can thwart chip security by altering individual flip-flop (FF) values. Different laser models, e.g., bit flip, bit set, and bit reset, have been developed to appreciate LFI on practical targets. As traditional approaches may incorporate substantial overhead, state-based SPARSE and transition-based TAMED countermeasures were proposed in our prior work to improve FSM resiliency efficiently. TAMED overcame SPARSE's limitation of being too conservative, and generating multiple LFI resilient encodings for contemporary LFI models on demand. SPARSE, however, incorporated design layout information into its vulnerability estimation which makes its vulnerability estimation metric more accurate. In this paper, we extend TAMED by proposing a transition-based encoding CAD framework (TRANSPOSE), that incorporates spatial transitional vulnerability metrics to quantify design susceptibility of FSMs based on both the bit flip model and the set-reset models. TRANSPOSE also incorporates floorplan optimization into its framework to accommodate secure spatial inter-distance of FF-sensitive regions. All TRANSPOSE approaches are demonstrated on 5 multifarious benchmarks and outperform existing FSM encoding schemes/frameworks in terms of security and overhead., Comment: 14 pages, 11 figures

Dry synthesis is a highly versatile method for the fabrication of nanoporous metal films, since it enables easy and reproducible deposition of single or multi-layer(s) of nanostructured materials that can find intriguing applications in plasmonics, photochemistry and photocatalysis, to name a few. Here, we extend the use of this methodology to the preparation of copper nanoislands that represent an affordable and versatile example of disordered plasmonic substrate. We perform detailed characterizations of the system using several techniques such as spectroscopic ellipsometry, cathodoluminescence, electron energy loss spectroscopy, ultrafast pump-probe spectroscopy and second-harmonic generation with the aim to investigate the optical properties of these systems in an unprecedented systematic way. Our study represents the starting point for future applications of this new disordered plasmonic system ranging from sensing to photochemistry and photocatalysis.

Ultrasound offers promising applications in biology and chemistry, but quantifying local ultrasound conditions remains challenging due to the lack of non-invasive measurement tools. We introduce antibubbles as novel optical reporters of local ultrasound pressure. These liquid-core, air-shell structures encapsulate fluorescent payloads, releasing them upon exposure to low-intensity ultrasound. We demonstrate their versatility by fabricating antibubbles with hydrophilic and hydrophobic payloads, revealing payload-dependent encapsulation efficiency and release dynamics. Using acoustic holograms, we showcase precise spatial control of payload release, enabling visualization of complex ultrasound fields. High-speed fluorescence imaging reveals a gentle, single-shot release mechanism occurring within 20-50 ultrasound cycles. It is thus possible to determine via an optical fluorescence marker what the applied ultrasound pressure was. This work thereby introduces a non-invasive method for mapping ultrasound fields in complex environments, potentially accelerating research in ultrasound-based therapies and processes. The long-term stability and versatility of these antibubble reporters suggest broad applicability in studying and optimizing ultrasound effects across various biological and chemical systems.

Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field., Comment: 33 pages, 17 figures

In 2017 Day et al. introduced the notion of locality as a structural complexity-measure for patterns in the field of pattern matching established by Angluin in 1980. In 2019 Casel et al. showed that determining the locality of an arbitrary pattern is NP-complete. Inspired by hierarchical clustering, we extend the notion to coloured graphs, i.e., given a coloured graph determine an enumeration of the colours such that colouring the graph stepwise according to the enumeration leads to as few clusters as possible. Next to first theoretical results on graph classes, we propose a priority search algorithm to compute the $k$-locality of a graph. The algorithm is optimal in the number of marking prefix expansions, and is faster by orders of magnitude than an exhaustive search. Finally, we perform a case study on a DBLP subgraph to demonstrate the potential of $k$-locality for knowledge discovery.

Accurate camera calibration is a well-known and widely used task in computer vision that has been researched for decades. However, the standard approach based on checkerboard calibration patterns has some drawbacks that limit its applicability. For example, the calibration pattern must be completely visible without any occlusions. Alternative solutions such as ChArUco boards allow partial occlusions, but require a higher camera resolution due to the fine details of the position encoding. We present a new calibration pattern that combines the advantages of checkerboard calibration patterns with a lightweight position coding that can be decoded at very low resolutions. The decoding algorithm includes error correction and is computationally efficient. The whole approach is backward compatible to both checkerboard calibration patterns and several checkerboard calibration algorithms. Furthermore, the method can be used not only for camera calibration but also for camera pose estimation and marker-based object localization tasks., Comment: To be published in German Conference on Pattern Recognition (GCPR) 2024. Further details: https://users.informatik.haw-hamburg.de/~stelldinger/pub/PuzzleBoard/

Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenarios and the hope is that the devices perceptive abilities provide an advantage over RGB-cameras. Even though some datasets exist, there is no standard benchmark available to systematically measure progress on this task and evaluate the benefit of hyperspectral data. In this paper, we work towards closing this gap by providing the HyperSpectral Semantic Segmentation benchmark (HS3-Bench). It combines annotated hyperspectral images from three driving scenario datasets and provides standardized metrics, implementations, and evaluation protocols. We use the benchmark to derive two strong baseline models that surpass the previous state-of-the-art performances with and without pre-training on the individual datasets. Further, our results indicate that the existing learning-based methods benefit more from leveraging additional RGB training data than from leveraging the additional hyperspectral channels. This poses important questions for future research on hyperspectral imaging for semantic segmentation in driving scenarios. Code to run the benchmark and the strong baseline approaches are available under https://github.com/nickstheisen/hyperseg., Comment: Accepted for publication at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Let $D$ be a digraph of order $n$ with adjacency matrix $A(D)$. For $\alpha\in[0,1)$, the $A_{\alpha}$ matrix of $D$ is defined as $A_{\alpha}(D)=\alpha {\Delta}^{+}(D)+(1-\alpha)A(D)$, where ${\Delta}^{+}(D)=\mbox{diag}~(d_1^{+},d_2^{+},\dots,d_n^{+})$ is the diagonal matrix of vertex outdegrees of $D$. Let $\sigma_{1\alpha}(D),\sigma_{2\alpha}(D),\dots,\sigma_{n\alpha}(D)$ be the singular values of $A_{\alpha}(D)$. Then the trace norm of $A_{\alpha}(D)$, which we call $\alpha$ trace norm of $D$, is defined as $\|A_{\alpha}(D)\|_*=\sum_{i=1}^{n}\sigma_{i\alpha}(D)$. In this paper, we find the singular values of some basic digraphs and characterize the digraphs $D$ with $\mbox{Rank}~(A_{\alpha}(D))=1$. As an application of these results, we obtain a lower bound for the trace norm of $A_{\alpha}$ matrix of digraphs and determine the extremal digraphs. In particular, we determine the oriented trees for which the trace norm of $A_{\alpha}$ matrix attains minimum. We obtain a lower bound for the $\alpha$ spectral norm $\sigma_{1\alpha}(D)$ of digraphs and characterize the extremal digraphs. As an application of this result, we obtain an upper bound for the $\alpha$ trace norm of digraphs and characterize the extremal digraphs., Comment: 19 pages, 1 figure with 11 digraphs

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examp

Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex functions with low computational cost, ML-based surrogates can accelerate scientific applications by replacing computationally intensive components with faster model inference. However, integrating ML models into these applications remains a significant challenge, hindering the widespread adoption of ML surrogates as an approximation technique in modern scientific computing. We propose an easy-to-use directive-based programming model that enables developers to seamlessly describe the use of ML models in scientific applications. The runtime support, as instructed by the programming model, performs data assimilation using the original algorithm and can replace the algorithm with model inference. Our evaluation across five benchmarks, testing over 5000 ML models, shows up to 83.6x speed improvements with minimal accuracy loss (as low as 0.01 RMSE)., Comment: 16 pages, 9 figures. Accepted at SC24

Visual SLAM is a key technology for many autonomous systems. However, tracking loss can lead to the creation of disjoint submaps in multimap SLAM systems like ORB-SLAM3. Because of that, these systems employ submap merging strategies. As we show, these strategies are not always successful. In this paper, we investigate the impact of using modern VPR approaches for submap merging in visual SLAM. We argue that classical evaluation metrics are not sufficient to estimate the impact of a modern VPR component on the overall system. We show that naively replacing the VPR component does not leverage its full potential without requiring substantial interference in the original system. Because of that, we present a post-processing pipeline along with a set of metrics that allow us to estimate the impact of modern VPR components. We evaluate our approach on the NCLT and Newer College datasets using ORB-SLAM3 with NetVLAD and HDC-DELF as VPR components. Additionally, we present a simple approach for combining VPR with temporal consistency for map merging. We show that the map merging performance of ORB-SLAM3 can be improved. Building on these results, researchers in VPR can assess the potential of their approaches for SLAM systems., Comment: Accepted at TAROS 2024. This is the submitted version

Class-incremental continual learning is an important area of research, as static deep learning methods fail to adapt to changing tasks and data distributions. In previous works, promising results were achieved using replay and compressed replay techniques. In the field of regular replay, GDumb achieved outstanding results but requires a large amount of memory. This problem can be addressed by compressed replay techniques. The goal of this work is to evaluate compressed replay in the pipeline of GDumb. We propose FETCH, a two-stage compression approach. First, the samples from the continual datastream are encoded by the early layers of a pre-trained neural network. Second, the samples are compressed before being stored in the episodic memory. Following GDumb, the remaining classification head is trained from scratch using only the decompressed samples from the reply memory. We evaluate FETCH in different scenarios and show that this approach can increase accuracy on CIFAR10 and CIFAR100. In our experiments, simple compression methods (e.g., quantization of tensors) outperform deep autoencoders. In the future, FETCH could serve as a baseline for benchmarking compressed replay learning in constrained memory scenarios., Comment: Submitted Version for IDEAL 2023

In the symposium contributions we discuss research in physics education and the consequences of its results for physics teaching. The symposium presents four different aspects of physics teaching and learning, but all of them have research-based problem analysis in common. The problems analysed cover different aspects of the physics teaching-learning process. Innovative aspects such as the effect on learning of the integration of engineering projects in the science teaching process, the influence on the learning process of conceptions about science and attitudes, and aspects related to teaching contents and students' learning difficulties. Its conclusions are not merely intuitive proposals based on teaching experience, but on a careful planning of data collection, analysis of results and empirical basis, Comment: Symposium Organized by GTG Physics Education Research at University-PERU

This paper presents a character-based approach for enhancing writer retrieval performance in the context of Greek papyri. Our contribution lies in introducing character-level annotations for frequently used characters, in our case the trigram kai and four additional letters (epsilon, kappa, mu, omega), in Greek texts. We use a state-of-the-art writer retrieval approach based on NetVLAD and compare a character-level-based feature aggregation method against the current default baseline of using small patches located at SIFT keypoint locations for building the page descriptors. We demonstrate that by using only about 15 characters per page, we are able to boost the performance up to 4% mAP (a relative improvement of 11%) on the GRK-120 dataset. Additionally, our qualitative analysis offers insights into the similarity scores of SIFT patches and specific characters. We publish the dataset with character-level annotations, including a quality label and our binarized images for further research., Comment: submitted to ICPR2024

Participatory design initiatives, especially within the realm of digital civics, are often integrated and co-developed with the very citizens and communities they intend to assist. Digital civics research aims to create positive social change using a variety of digital technologies. These research projects commonly adopt various embedded processes, such as commissioning models \cite{dcitizensproj22}. Despite the adoption of this process within a range of domains, there isn't currently a framework for best practices and accountability procedures to ensure we engage with citizens ethically and ensure the sustainability of our projects. This workshop aims to provide a space to start collaboratively constructing a dynamic framework of best practices, laying the groundwork for the future of sustainable embedded research processes. The overarching goal is to foster discussions and share insights that contribute to developing effective practices, ensuring the longevity and impact of participatory digital civics projects.

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study., Comment: Accepted to FG 2024

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM., Comment: 10 Pages, 6 Figures

ChatGPT, the AI-powered chatbot with a massive user base of hundreds of millions, has become a global phenomenon. However, the use of Conversational AI Systems (CAISs) like ChatGPT for research in the field of Social Simulation is still limited. Specifically, there is no evidence of its usage in Agent-Based Social Simulation (ABSS) model design. While scepticism towards anything new is inherent to human nature, we firmly believe it is imperative to initiate the use of this innovative technology to support ABSS model design. This paper presents a proof-of-concept that demonstrates how CAISs can facilitate the development of innovative conceptual ABSS models in a concise timeframe and with minimal required upfront case-based knowledge. By employing advanced prompt engineering techniques and adhering to the Engineering ABSS framework, we have constructed a comprehensive prompt script that enables the design of ABSS models with or by the CAIS. The effectiveness of the script is demonstrated through an illustrative case study concerning the use of adaptive architecture in museums. Despite occasional inaccuracies and divergences in conversation, the CAIS proved to be a valuable companion for ABSS modellers., Comment: 29 pages, 3 figures, 1 table

De Bruijn tori, also called perfect maps, are two-dimensional periodic arrays of letters drawn from a given finite alphabet, such that each possible pattern of a given shape $(m,n)$ appears exactly once within one period of the torus. It is still unknown if de Bruijn tori of some certain size exist, like e.g. square shaped de Bruijn Tori with odd $m=n\in\{3,5,7\}$ and an even alphabet size $k$. However, in certain applications like positional coding, sub-perfect maps are sufficient, i.e. one does not need every possible $(m,n)$-pattern to appear, as long as a sufficient large number of such patterns is captured and every pattern occurs at most once. We show, that given any $m=n$ and a square alphabet size $k^2$, one can efficiently construct a sub-perfect map which is almost perfect, i.e. of almost maximal size. We do this by introducing de Bruijn rings, i.e. sub-perfect maps of minimal height, and providing an efficient construction method for them. We extend our results to non-square torus shapes and arbitrary non-prime alphabet sizes.

This paper introduces SAGHOG, a self-supervised pretraining strategy for writer retrieval using HOG features of the binarized input image. Our preprocessing involves the application of the Segment Anything technique to extract handwriting from various datasets, ending up with about 24k documents, followed by training a vision transformer on reconstructing masked patches of the handwriting. SAGHOG is then finetuned by appending NetRVLAD as an encoding layer to the pretrained encoder. Evaluation of our approach on three historical datasets, Historical-WI, HisFrag20, and GRK-Papyri, demonstrates the effectiveness of SAGHOG for writer retrieval. Additionally, we provide ablation studies on our architecture and evaluate un- and supervised finetuning. Notably, on HisFrag20, SAGHOG outperforms related work with a mAP of 57.2 % - a margin of 11.6 % to the current state of the art, showcasing its robustness on challenging data, and is competitive on even small datasets, e.g. GRK-Papyri, where we achieve a Top-1 accuracy of 58.0%., Comment: accepted for ICDAR2024

Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://github.com/LSIbabnikz/AI-KD., Comment: IEEE International Workshop on Biometrics and Forensics (IWBF) 2024, pp. 6

Hafnium Nitride (HfN) is a promising and very robust alternative to gold for applications of nanoscale metals. Details of the nanomorphology related to variations in strain states and optical properties can be crucial for applications in nanophotonics and plasmon-assisted chemistry. We use ultrafast reciprocal space mapping (URSM) with hard x-rays to unveil the nanomorphology of thin HfN films. Static high-resolution x-ray diffraction reveals a twofold composition of the thin films being separated into regions with identical lattice constant and similar out-of-plane but hugely different in-plane coherence lengths. URSM upon femtosecond laser excitation reveals different transient strain dynamics for the two respective Bragg peak components. This unambiguously locates the longer in-plane coherence length in the first 15\,nm of the thin film adjacent to the substrate. The transient shift of the broad diffraction peak displays the strain dynamics of the entire film, implying that the near-substrate region hosts nanocrystallites with small and large coherence length, whereas the upper part of the film grows in small columnar grains. Our results illustrate that URSM is a suitable technique for non-destructive investigations of the depth-resolved nanomorphology of nanostructures., Comment: 7 pages, 4 Figures

Mountain slopes are perfect examples of harsh environments in which humans are required to perform difficult and dangerous operations such as removing unstable boulders, dangerous vegetation or deploying safety nets. A good replacement for human intervention can be offered by climbing robots. The different solutions existing in the literature are not up to the task for the difficulty of the requirements (navigation, heavy payloads, flexibility in the execution of the tasks). In this paper, we propose a robotic platform that can fill this gap. Our solution is based on a robot that hangs on ropes, and uses a retractable leg to jump away from the mountain walls. Our package of mechanical solutions, along with the algorithms developed for motion planning and control, delivers swift navigation on irregular and steep slopes, the possibility to overcome or travel around significant natural barriers, and the ability to carry heavy payloads and execute complex tasks. In the paper, we give a full account of our main design and algorithmic choices and show the feasibility of the solution through a large number of physically simulated scenarios.

Large-scale applications of Visual Place Recognition (VPR) require computationally efficient approaches. Further, a well-balanced combination of data-based and training-free approaches can decrease the required amount of training data and effort and can reduce the influence of distribution shifts between the training and application phases. This paper proposes a runtime and data-efficient hierarchical VPR pipeline that extends existing approaches and presents novel ideas. There are three main contributions: First, we propose Local Positional Graphs (LPG), a training-free and runtime-efficient approach to encode spatial context information of local image features. LPG can be combined with existing local feature detectors and descriptors and considerably improves the image-matching quality compared to existing techniques in our experiments. Second, we present Attentive Local SPED (ATLAS), an extension of our previous local features approach with an attention module that improves the feature quality while maintaining high data efficiency. The influence of the proposed modifications is evaluated in an extensive ablation study. Third, we present a hierarchical pipeline that exploits hyperdimensional computing to use the same local features as holistic HDC-descriptors for fast candidate selection and for candidate reranking. We combine all contributions in a runtime and data-efficient VPR pipeline that shows benefits over the state-of-the-art method Patch-NetVLAD on a large collection of standard place recognition datasets with 15$\%$ better performance in VPR accuracy, 54$\times$ faster feature comparison speed, and 55$\times$ less descriptor storage occupancy, making our method promising for real-world high-performance large-scale VPR in changing environments. Code will be made available with publication of this paper., Comment: IEEE Robotics and Automation Letters (RA-L)

Additive manufacturing (AM) techniques have been used to enhance the design and fabrication of complex components for various applications in the medical, aerospace, energy, and consumer products industries. A defining feature for many AM parts is the complex internal geometry enabled by the printing process. However, inspecting these internal structures requires volumetric imaging, i.e., X-ray CT, leading to the well-known challenge of visualizing complex 3D geometries using 2D desktop interfaces. Furthermore, existing tools are limited to single-user systems making it difficult to jointly discuss or share findings with a larger team, i.e., the designers, manufacturing experts, and evaluation team. In this work, we present a collaborative virtual reality (VR) for the exploration and inspection of AM parts. Geographically separated experts can virtually inspect and jointly discuss data. It also supports VR and non-VR users, who can be spectators in the VR environment. Various features for data exploration and inspection are developed and enhanced via real-time synchronization. We followed usability and interface verification guidelines using Nielsen's heuristics approach. Furthermore, we conducted exploratory and semi-structured interviews with domain experts to collect qualitative feedback. Results reveal potential benefits, applicability, and current limitations. The proposed collaborative VR environment provides a new basis and opens new research directions for virtual inspection and team collaboration in AM settings., Comment: Conditionally Accepted - CHI LBW 2024