OpenRaiser/Intern-Atlas huggingface dataset:
A MAPE-K-Based Method for Architectural Conformance Checking in Self-Adaptive Systems
Venue: arXiv cs.SE 2025
Year: 2025

Self-adaptive systems (SASs) adjust their behavior at runtime in response to internal or external change. The MAPE-K model, which includes Monitors, Analyzers, Planners, Executors, and shared Knowledge, is a reference for structuring feedback loops. As SASs evolve, implementations can drift from the intended MAPE-K architecture, compromising planned quality attributes. Architectural Conformance Checking (ACC) addresses this risk by comparing the current implementation to a specification of the architecture. General purpose ACC techniques are flexible, but lack SAS specific semantics, leading to ambiguous specifications and missed violations. We present REMEDY, an ACC approach designed for MAPE-K based SASs. REMEDY provides three elements: a domain specific language for expressing planned architectures in MAPE-K terms, a tool that extracts the implemented architecture, and a conformance engine that reports violations. By encoding SAS domain rules and reusing MAPE-K abstractions, REMEDY reduces specification effort and lowers error rates relative to general ACC. We evaluate REMEDY through a robotic SAS case study and a controlled experiment with software engineering students. Results show higher modeling productivity and effective detection of architectural drift, supporting more reliable verification of conformance to the MAPE-K reference model.
A Causal Model for Quantifying Multipartite Classical and Quantum Correlations
Venue: arXiv cs.IT 2025
Year: 2025

We give an operational definition of information-theoretic resources within a given multipartite classical or quantum correlation. We present our causal model that serves as the source coding side of this correlation and introduce a novel concept of resource rate. We argue that, beyond classical secrecy, additional resources exist that are useful for the security of distributed computing problems, which can be captured by the resource rate. Furthermore, we establish a relationship between resource rate and an extension of Shannon's logarithmic information measure, namely, total correlation.
Chronicling Germany: An Annotated Historical Newspaper Dataset
Venue: arXiv cs.DL 2025
Year: 2025

The correct detection of dense article layout and the recognition of characters in historical newspaper pages remains a challenging requirement for Natural Language Processing (NLP) and machine learning applications on historical newspapers in the field of digital history. Digital newspaper portals for historic Germany typically provide Optical Character Recognition (OCR) text, albeit of varying quality. Unfortunately, layout information is often missing, limiting this rich source's scope. Our dataset is designed to enable the training of layout and OCR modells for historic German-language newspapers. The Chronicling Germany dataset contains 693 annotated historical newspaper pages from the time period between 1852 and 1924. The paper presents a processing pipeline and establishes baseline results on in- and out-of-domain test data using this pipeline. Both our dataset and the corresponding baseline code are freely available online. This work creates a starting point for future research in the field of digital history and historic German language newspaper processing. Furthermore, it provides the opportunity to study a low-resource task in computer vision
Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI
Venue: arXiv cs.CV 2025
Year: 2025

A comprehensive understanding of the organizational principles in the human brain requires, among other factors, well-quantifiable descriptors of nerve fiber architecture. Three-dimensional polarized light imaging (3D-PLI) is a microscopic imaging technique that enables insights into the fine-grained organization of myelinated nerve fibers with high resolution. Descriptors characterizing the fiber architecture observed in 3D-PLI would enable downstream analysis tasks such as multimodal correlation studies, clustering, and mapping. However, best practices for observer-independent characterization of fiber architecture in 3D-PLI are not yet available. To this end, we propose the application of a fully data-driven approach to characterize nerve fiber architecture in 3D-PLI images using self-supervised representation learning. We introduce a 3D-Context Contrastive Learning (CL-3D) objective that utilizes the spatial neighborhood of texture examples across histological brain sections of a 3D reconstructed volume to sample positive pairs for contrastive learning. We combine this sampling strategy with specifically designed image augmentations to gain robustness to typical variations in 3D-PLI parameter maps. The approach is demonstrated for the 3D reconstructed occipital lobe of a vervet monkey brain. We show that extracted features are highly sensitive to different configurations of nerve fibers, yet robust to variations between consecutive brain sections arising from histological processing. We demonstrate their practical applicability for retrieving clusters of homogeneous fiber architecture, performing classification with minimal annotations, and query-based retrieval of characteristic components of fiber architecture such as U-fibers.
Through-Wall Imaging based on WiFi Channel State Information
Venue: arXiv cs.CV 2025
Year: 2025

This work presents a seminal approach for synthesizing images from WiFi Channel State Information (CSI) in through-wall scenarios. Leveraging the strengths of WiFi, such as cost-effectiveness, illumination invariance, and wall-penetrating capabilities, our approach enables visual monitoring of indoor environments beyond room boundaries and without the need for cameras. More generally, it improves the interpretability of WiFi CSI by unlocking the option to perform image-based downstream tasks, e.g., visual activity recognition. In order to achieve this crossmodal translation from WiFi CSI to images, we rely on a multimodal Variational Autoencoder (VAE) adapted to our problem specifics. We extensively evaluate our proposed methodology through an ablation study on architecture configuration and a quantitative/qualitative assessment of reconstructed images. Our results demonstrate the viability of our method and highlight its potential for practical applications.
Gradient-Free Score-Based Sampling Methods with Ensembles
Venue: arXiv cs.LG 2025
Year: 2025

Recent developments in generative modeling have utilized score-based methods coupled with stochastic differential equations to sample from complex probability distributions. However, these and other performant sampling methods generally require gradients of the target probability distribution, which can be unavailable or computationally prohibitive in many scientific and engineering applications. Here, we introduce ensembles within score-based sampling methods to develop gradient-free approximate sampling techniques that leverage the collective dynamics of particle ensembles to compute approximate reverse diffusion drifts. We introduce the underlying methodology, emphasizing its relationship with generative diffusion models and the previously introduced Föllmer sampler. We demonstrate the efficacy of the ensemble strategies through various examples, ranging from low- to medium-dimensionality sampling problems, including multi-modal and highly non-Gaussian probability distributions, and provide comparisons to traditional methods like the No-U-Turn Sampler. Additionally, we showcase these strategies in the context of a high-dimensional Bayesian inversion problem within the geophysical sciences. Our findings highlight the potential of ensemble strategies for modeling complex probability distributions in situations where gradients are unavailable.
Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection
Venue: arXiv cs.RO 2025
Year: 2025

Mobile service robots are increasingly prevalent in human-centric, real-world domains, operating autonomously in unconstrained indoor environments. In such a context, robotic vision plays a central role in enabling service robots to perceive high-level environmental features from visual observations. Despite the data-driven approaches based on deep learning push the boundaries of vision systems, applying these techniques to real-world robotic scenarios presents unique methodological challenges. Traditional models fail to represent the challenging perception constraints typical of service robots and must be adapted for the specific environment where robots finally operate. We propose a method leveraging photorealistic simulations that balances data quality and acquisition costs for synthesizing visual datasets from the robot perspective used to train deep architectures. Then, we show the benefits in qualifying a general detector for the target domain in which the robot is deployed, showing also the trade-off between the effort for obtaining new examples from such a setting and the performance gain. In our extensive experimental campaign, we focus on the door detection task (namely recognizing the presence and the traversability of doorways) that, in dynamic settings, is useful to infer the topology of the map. Our findings are validated in a real-world robot deployment, comparing prominent deep-learning models and demonstrating the effectiveness of our approach in practical settings.
CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration
Venue: arXiv cs.CV 2025
Year: 2025

LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in LiDAR maps using inexpensive cameras is a cost-effective alternative for large-scale deployment. Nevertheless, most existing approaches struggle to generalize to new sensor setups and environments, requiring retraining or fine-tuning. In this paper, we present CMRNext, a novel approach for camera-LIDAR matching that is independent of sensor-specific parameters, generalizable, and can be used in the wild for monocular localization in LiDAR maps and camera-LiDAR extrinsic calibration. CMRNext exploits recent advances in deep neural networks for matching cross-modal data and standard geometric techniques for robust pose estimation. We reformulate the point-pixel matching problem as an optical flow estimation problem and solve the Perspective-n-Point problem based on the resulting correspondences to find the relative pose between the camera and the LiDAR point cloud. We extensively evaluate CMRNext on six different robotic platforms, including three publicly available datasets and three in-house robots. Our experimental evaluations demonstrate that CMRNext outperforms existing approaches on both tasks and effectively generalizes to previously unseen environments and sensor setups in a zero-shot manner. We make the code and pre-trained models publicly available at http://cmrnext.cs.uni-freiburg.de .
Behind the Myth of Exploration in Policy Gradients
Venue: arXiv cs.LG 2025
Year: 2025

In order to compute near-optimal policies with policy-gradient algorithms, it is common in practice to include intrinsic exploration terms in the learning objective. Although the effectiveness of these terms is usually justified by an intrinsic need to explore environments, we propose a novel analysis with the lens of numerical optimization. Two criteria are introduced on the learning objective and two others on its stochastic gradient estimates, and are afterwards used to discuss the quality of the policy after optimization. The analysis sheds light on two separate effects of exploration techniques. First, they make it possible to smooth the learning objective and to eliminate local optima while preserving the global maximum. Second, they modify the gradient estimates, increasing the probability that the stochastic parameter updates eventually provide an optimal policy. We empirically illustrate these effects with exploration strategies based on entropy bonuses, identifying limitations and suggesting directions for future work.
Survey of Privacy Threats and Countermeasures in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among typical types of federated learning have not been categorized and described in a comprehensive and specific way. In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.
Generalized Arlery-Tan-Rabaste-Levenshtein Lower Bounds on Ambiguity Function and Their Asymptotic Achievability
Venue: arXiv cs.IT 2025
Year: 2025

This paper presents generalized Arlery-Tan-Rabaste-Levenshtein lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). Our core idea is to explore the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices by introducing two weight vectors associated with the delay and Doppler shifts, respectively. As a second major contribution, we demonstrate that our derived lower bounds are asymptotically achievable with selected Chu sequence sets by analyzing their maximum auto- and cross- AF magnitudes within certain LAZ.
Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey
Venue: arXiv cs.SE 2025
Year: 2025

Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address the challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. The solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile software development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success.
A Framework for Building Point Cloud Cleaning, Plane Detection and Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a framework to address the challenges involved in building point cloud cleaning, plane detection, and semantic segmentation, with the ultimate goal of enhancing building modeling. We focus in the cleaning stage on removing outliers from the acquired point cloud data by employing an adaptive threshold technique based on z-score measure. Following the cleaning process, we perform plane detection using the robust RANSAC paradigm. The goal is to carry out multiple plane segmentations, and to classify segments into distinct categories, such as floors, ceilings, and walls. The resulting segments can generate accurate and detailed point clouds representing the building's architectural elements. Moreover, we address the problem of semantic segmentation, which plays a vital role in the identification and classification of different components within the building, such as walls, windows, doors, roofs, and objects. Inspired by the PointNet architecture, we propose a deep learning architecture for efficient semantic segmentation in buildings. The results demonstrate the effectiveness of the proposed framework in handling building modeling tasks, paving the way for improved accuracy and efficiency in the field of building modelization.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management.
Allocation of Indivisible Items with a Common Preference Graph: Minimizing Total Dissatisfaction
Venue: arXiv cs.GT 2025
Year: 2025

Allocating indivisible items among a set of agents is a frequently studied discrete optimization problem. In the setting considered in this work, the agents' preferences over the items are assumed to be identical. We consider a very recent measure for the overall quality of an allocation which does not rely on numerical valuations of the items. Instead, it captures the agents' opinion by a directed acyclic preference graph with vertices representing items. An arc $(a,b)$ in such a graph means that the agents prefer item $a$ over item $b$. For a given allocation of items the dissatisfaction of an agent is defined as the number of items which the agent does not receive and for which no more preferred item is given to the agent. Our goal is to find an efficient allocation of the items to the agents such that the total dissatisfaction over all agents is minimized. We explore the dichotomy between NP-hard and polynomially solvable instances, depending on properties of the underlying preference graph. While the problem is NP-hard already for three agents even on very restricted graph classes, it is polynomially solvable for two agents on general preference graphs. For an arbitrary number of agents, we derive polynomial-time algorithms for relevant restrictions of the underlying undirected graph. These are trees and, among the graphs of treewidth two, series-parallel graphs and cactus graphs.
Towards Understanding the Challenges of Bug Localization in Deep Learning Systems
Venue: arXiv cs.SE 2025
Year: 2025

Software bugs cost the global economy billions of dollars annually and claim ~50\% of the programming time from software developers. Locating these bugs is crucial for their resolution but challenging. It is even more challenging in deep-learning systems due to their black-box nature. Bugs in these systems are also hidden not only in the code but also in the models and training data, which might make traditional debugging methods less effective. In this article, we conduct a large-scale empirical study to better understand the challenges of localizing bugs in deep-learning systems. First, we determine the bug localization performance of four existing techniques using 2,365 bugs from deep-learning systems and 2,913 from traditional software. We found these techniques significantly underperform in localizing deep-learning system bugs. Second, we evaluate how different bug types in deep learning systems impact bug localization. We found that the effectiveness of localization techniques varies with bug type due to their unique challenges. For example, tensor bugs were more accessible to locate due to their structural nature, while all techniques struggled with GPU bugs due to their external dependencies. Third, we investigate the impact of bugs' extrinsic nature on localization in deep-learning systems. We found that deep learning bugs are often extrinsic and thus connected to artifacts other than source code (e.g., GPU, training data), contributing to the poor performance of existing localization methods.
Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
Venue: arXiv cs.RO 2025
Year: 2025

We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Network that predicts relevant interactions along the MPC prediction horizon between the autonomous vehicle and the surrounding vehicles using Lagrangian duality, and 2) a reduced Stochastic MPC problem that eliminates irrelevant collision avoidance constraints, enhancing computational efficiency. Our approach is demonstrated in a simulated traffic intersection with interactive surrounding vehicles, showcasing a 12x speed-up in solving the motion planning problem. A video demonstrating the proposed architecture in multiple complex traffic scenarios can be found here: https://youtu.be/-pRiOnPb9_c. GitHub: https://github.com/MPC-Berkeley/hmpc_raidnet