arXiv subjects

Christoph Lange

Publications and source records attributed to Christoph Lange.

At least 19 recordsLinked to original sources

arXiv2026

What Does ODRL Mean? A Cross-Level Ontological Grounding of Permissions, Prohibitions, and Duties in UFO-L

ODRL policy evaluators produce verdicts, but say nothing about the normative positions a policy brings into existence, the authority structures those positions presuppose, or who holds the power to declare a norm violated. We formulate the Cross-Level Design Principle: any normative language with violable, consequential norms requires both conduct-level positions (Permission, Duty, Right, No right) and competence-level positions (Power, Subjection, Immunity, Disability). Applying this to ODRL, we establish that prohibition is sanctioned (violation possible and consequential), that permission is underspecified across its behaviour parameter (open vs. closed world), and that the formal semantics covers achievement obligations only. We ground ODRL in UFO-L, mapping each activated rule to a simple legal relator and extending coverage from two to eight legal positions; violation-declaration authority, implicit in every existing evaluator, becomes an explicit Power-Subjection pair. All axioms are mechanically verified in Isabelle/HOL and across a 39-problem benchmark under Vampire, E, and Z3.

cs.LO↗

arXiv2026

Sort-Stratified Semantics for Temporal Conflict Detection in ODRL Policies

In the Open Digital Rights Language (ODRL), temporal constraints range over two sorts, instants and durations, but the comparison operators do not distinguish them. The same operator thus means "earlier instant" or "shorter duration," leaving conflict detection between two policies unsound. We resolve this by sort stratification: each temporal operand is typed to one of two ordered domains, points in time or amounts of time. Each constraint then denotes an interval, and conflict reduces to interval comparison under a three-valued verdict (Conflict, Compatible, Unknown). We characterise the check's decidability across a static and a runtime fragment, prove it sound, and evaluate it on a benchmark of policy problems compiled to TPTP and SMT-LIB, available as an artefact.

cs.LO↗

arXiv2026

Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge

Scientific knowledge on the Web is published as passive assertions and cannot decide when to validate evidence, reconcile contradictions, or update confidence as findings accumulate. Curation depends on centralised middleware and institutional continuity, but when registries close, active stewardship stops even when data remain online. We advance the concept of Autonomous FAIR Digital Objects (aFDOs) from an abstract idea to an operational model, to offer a route from passive scientific publication toward accountable, standards-aligned automation that can outlive its publishing institutions. aFDO augments FDOs with three capabilities anchored in Semantic Web standards, namely 1) a policy layer over RDF-star aligned with PROV-O, SHACL, and ODRL for portable condition-action rules, 2) an announcement layer over ActivityStreams 2.0 that bounds per-announcement evaluation cost, and 3) an agreement layer that resolves multi-source contradictions through reputation and confidence weighted agreement under a bounded adversarial model. We provide a formal definition that distinguishes policy specifications, event handlers, and communication interfaces. We evaluate an open reference implementation on 4,305 FDOs grounded in rare-disease ontologies, namely ClinVar, HPO, and Orphanet, combined with controlled synthetic observations. The consensus mechanism resolves 56.3% of 3,914 naturally occurring ClinVar conflicts where multiple submitters disagree and an expert panel has subsequently adjudicated. Under Sybil, collusion, and poisoning attacks, the mechanism degrades gracefully within its design Byzantine-tolerance bound (f < n/5), and fails as predicted beyond that bound.

cs.AI↗

arXiv2026

RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.

cs.LG↗

arXiv2026

Axis-Aligned Semantics for ODRL: Resolving Dimensional Ambiguity in Policy Constraints

The Open Digital Rights Language (ODRL) represents policy constraints as triples of a left operand, an operator, and a value. Several spatial operands, however, range over multi-axis domains such as width, height, and depth, while the constraint syntax provides no explicit axis identity. As a result, policy engines cannot determine whether multiple constraints apply to the same axis or different ones, making conflict detection unsound or incomplete. We resolve this ambiguity by axis decomposition, replacing multi-axis operands with axis-specific scalar operands over totally ordered domains. Each constraint then denotes an interval per axis and each policy an axis-aligned box, reducing conflict detection to box comparison. We define a three-valued semantics (Conflict, Compatible, Unknown), prove the decomposition sound and backward compatible with ODRL, instantiate it as ODRL Axis-Aligned Profile (OAAP), and validate it on a benchmark of 256 ODRL policy problems, each expressed in Turtle and compiled to first-order (TPTP) and SMT-LIB form, using Vampire, E, Z3, and cvc5.

cs.CL↗

arXiv2026

Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations

In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As it records vibrational modes of molecules it provides that information non-invasively in a single spectrum. Typically, partial least squares (PLS) is the model of choice to infer information about variables of interest from the spectra. However, biological processes are known for their complexity where convolutional neural networks (CNN) present a powerful alternative. They can handle non-Gaussian noise and account for beam misalignment, pixel malfunctions or the presence of additional substances. However, they require a lot of data during model training, and they pick up non-linear dependencies in the process variables. In this work, we exploit the additive nature of spectra in order to generate additional data points from a given dataset that have statistically independent labels so that a network trained on such data exhibits low correlations between the model predictions. We show that training a CNN on these generated data points improves the performance on datasets where the annotations do not bear the same correlation as the dataset that was used for model training. This data augmentation technique enables us to reuse spectra as training data for new contexts that exhibit different correlations. The additional data allows for building a better and more robust model. This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training. We demonstrate the capabilities of the proposed method using synthetic spectra of Ralstonia eutropha batch cultivations to monitor substrate, biomass and polyhydroxyalkanoate (PHA) biopolymer concentrations during of the experiments.

cs.LG↗

arXiv2025

All-optical band structure reconstruction and onset of Landau quantization of Dirac fermions

The nature of relativistic electrons in solids depends on the precise shape of the underlying band structure. Prominently, symmetry-related mechanisms, such as the breaking of time reversal symmetry in topological insulators, can lead to the emergence of band gaps on small energy scales. It is, thus, important to quantify potential gaps of the Dirac cone with meV precision. Yet established band structure measurements are often challenged by their strict surface sensitivity or limited energy resolution. In this work, we use broadband, time-resolved THz magneto-spectroscopy to access the band structure of Dirac electrons in a buried HgTe quantum well by contact-free, all-optical measurements. Optical doping allows us to control the Fermi level without applying any electrical gate voltages. The background-free measurement of the cyclotron resonance of the Dirac system over 2.5 optical octaves, a broad range of magnetic field strengths, and different Fermi energies allows us to reconstruct the band structure near the Dirac point with sub-meV precision, and to observe a crossover of Landau quantization from a quasi-classical to the relativistic regime.

cond-mat.mes-hall↗

arXiv2025

FDO Manager: Minimum Viable FAIR Digital Object Implementation

In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents "FDO Manager" a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.

cs.DC↗

arXiv2025

A Service Architecture for Dataspaces

Dataspaces are designed to support sovereign, trusted and decentralized data exchange between participants forming an ecosystem. They are standardized by initiatives such as the International Data Spaces Association or Gaia-X and have gained adoption in several domains such as mobility, manufacturing, tourism or culture. In dataspaces, participants use connectors to communicate peer-to-peer. The Eclipse Dataspace Components (EDC) Connector is a broadly adopted, open-source implementation that adheres to the standards and is supported by a large community. As dataspaces in general, it focuses on the exchange of data assets with associated usage policies and does not support services. In practice, however, there is demand for dataspace-based services and conceptual arguments support their inclusion in dataspaces. In this paper, we propose an abstraction layer for providing generic services within dataspaces. Adopters can use this layer to easily develop own services, seamlessly integrated with the existing dataspace technology. Besides, we present an initial implementation of this service architecture for the EDC Connector and demonstrate its practical applicability.

cs.DB↗

arXiv2025

From Instructions to ODRL Usage Policies: An Ontology Guided Approach

This study presents an approach that uses large language models such as GPT-4 to generate usage policies in the W3C Open Digital Rights Language ODRL automatically from natural language instructions. Our approach uses the ODRL ontology and its documentation as a central part of the prompt. Our research hypothesis is that a curated version of existing ontology documentation will better guide policy generation. We present various heuristics for adapting the ODRL ontology and its documentation to guide an end-to-end KG construction process. We evaluate our approach in the context of dataspaces, i.e., distributed infrastructures for trustworthy data exchange between multiple participating organizations for the cultural domain. We created a benchmark consisting of 12 use cases of varying complexity. Our evaluation shows excellent results with up to 91.95% accuracy in the resulting knowledge graph.

cs.CL↗

arXiv2025

Automatic Raman Measurements in a High-Throughput Bioprocess Development Lab

This study presents a collection of physical devices and software services that fully automate Raman spectra measurements for liquid samples within a robotic facility. This method is applicable to various fields, with demonstrated efficacy in biotechnology, where Raman spectroscopy monitors substrates, metabolites, and product-related concentrations. Our system specifically measures 50 $\micro L$ samples using a liquid handling robot capable of taking 8 samples simultaneously. We record multiple Raman spectra for 10s each. Furthermore, our system takes around 20s for sample handling, cleaning, and preparation of the next measurement. All spectra and metadata are stored in a database, and we use a machine learning model to estimate concentrations from the spectra. This automated approach enables gathering spectra for various applications under uniform conditions in high-throughput fermentation processes, calibration procedures, and offline evaluations. This allows data to be combined to train sophisticated machine learning models with improved generalization. Consequently, we can develop accurate models more quickly for new applications by reusing data from prior applications, thereby reducing the need for extensive calibration data.

q-bio.QM↗

arXiv2025

mobilityDCAT-AP: a Metadata Specification for Enhanced Cross-border Mobility Data Sharing

Integrated and efficient mobility requires data sharing among the involved stakeholders. In this direction, regulators and transport authorities have been defining policies to foster the digitalisation and online publication of mobility data. However, the creation of several heterogeneous data portals for mobility data resulted in a fragmented ecosystem that challenges data accessibility. In this context, metadata is a key enabler to foster the findability and reusability of relevant datasets, but their interoperability across different data portals should be ensured. Moreover, each domain presents specificities on the relevant information that should be encoded through metadata. To solve these issues within the mobility domain, we present mobilityDCAT-AP, a reference metadata specification for mobility data portals specified by putting together domain experts and the Semantic Web community. We report on the work done to develop the metadata model behind mobilityDCAT-AP and the best practices followed in its implementation and publication. Finally, we describe the available educational resources and the activities performed to ensure broader adoption of mobilityDCAT-AP across mobility data portals. We present success stories from early adopters and discuss the challenges they encountered in implementing a metadata specification based on Semantic Web technologies.

cs.DB↗

arXiv2025

XFSC: A Catalogue of Trustable Semantic Metadata for Data Services and Providers

In dataspaces, federation services facilitate key functions such as enabling participating organizations to establish mutual trust and assisting them in discovering data and services available for consumption. Discovery is enabled by a catalogue, where participants publish metadata describing themselves and their data and service offerings as Verifiable Presentations (VPs), such that other participants may query them. This paper presents the Eclipse Cross Federation Services Components (XFSC) Catalogue, which originated as a catalogue reference implementation for the Gaia-X federated cloud service architecture but is also generally applicable to metadata required to be trustable. This implementation provides basic lifecycle management for DCAT-style metadata records and schemas. It validates submitted VPs for their cryptographic integrity and trustability, and for their conformance to an extensible collection of semantic schemas. The claims in the latest versions of valid VP submissions are extracted into a searchable graph database. The implementation scales to large numbers of records and is secure by design. Filling the catalogue with content in a maintainable way requires bindings towards where data and service offerings are coming from: connectors that expose resources hosted in an organization's IT infrastructure towards the dataspace. We demonstrate the integration of our catalogue with the widely used Eclipse Dataspace Components Connector, enabling real-world use cases of the German Culture Dataspace. In addition, we discuss potential extensions and upcoming integrations of the catalogue.

cs.DB↗

arXiv2024

From Theory to Practice: Demonstrators of FAIR Data Spaces Across Different Sectors

The principles of data spaces for sovereign data exchange across trusted organizations have so far mainly been adopted in business-to-business settings, and recently scaled to cloud environments. Meanwhile, research organizations have established distributed research data infrastructures, respecting the principle that data must be FAIR, i.e., findable, accessible, interoperable and reusable. For mutual benefit of these two communities, the FAIR Data Spaces project aims to connect them towards the vision of a common, cloud-based data space for industry and research. Thus, the project establishes a common legal and ethical framework, common technical building blocks, and it demonstrates the orchestration of multiple building blocks in self-contained settings addressing a diverse range of use cases in domains including health, biodiversity, and engineering. This paper gives a summary of all demonstrators, ranging from research data infrastructures scaled to industry-ready cloud environments to work in progress on building bridges between operational business-to-business data spaces and research data infrastructures.

cs.DC↗

arXiv2024

Towards Enabling FAIR Dataspaces Using Large Language Models

Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the question of how these models can support the adoption of FAIR dataspaces. In this work, we demonstrate the potential of LLMs in dataspaces with a concrete example. We also derive a research agenda for exploring this emerging field.

cs.CL↗

arXiv2024

Data Trading and Monetization: Challenges and Open Research Directions

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking.

cs.DC↗

arXiv2023

Sculpting ultrastrong light-matter coupling through spatial matter structuring

The central theme of cavity quantum electrodynamics is the coupling of a single optical mode with a single matter excitation, leading to a doublet of cavity polaritons which govern the optical properties of the coupled structure. Especially in the ultrastrong coupling regime, where the ratio of the vacuum Rabi frequency and the quasi-resonant carrier frequency of light, $Ω_{\mathrm R}/ω_{\mathrm c}$, approaches unity, the polariton doublet bridges a large spectral bandwidth $2Ω_{\mathrm R}$, and further interactions with off-resonant light and matter modes may occur. The resulting multi-mode coupling has recently attracted attention owing to the additional degrees of freedom for designing light-matter coupled resonances, despite added complexity. Here, we experimentally implement a novel strategy to sculpt ultrastrong multi-mode coupling by tailoring the spatial overlap of multiple modes of planar metallic THz resonators and the cyclotron resonances of Landau-quantized two-dimensional electrons, on subwavelength scales. We show that similarly to the selection rules of classical optics, this allows us to suppress or enhance certain coupling pathways and to control the number of light-matter coupled modes, their octave-spanning frequency spectra, and their response to magnetic tuning. This offers novel pathways for controlling dissipation, tailoring quantum light sources, nonlinearities, correlations as well as entanglement in quantum information processing.

quant-ph↗

arXiv2023

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.

cs.DB↗