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Item type: Item , A Practical HPO Cookbook(Hannover : Technische Informationsbibliothek, 2026-08-14) Lindauer, Marius; Tornede, Alexander; Wever, MarcelOn the one hand, hyperparameter optimization (HPO) can be crucial to leverage the full potential of machine learning models and pipelines, even in the age of agentic workflows and foundation models. On the other hand, HPO can be quite frustrating for new users because the tools suggest very simple use, but in practice, we need to follow sound workflows to ensure reliable results that actually pay off and do not waste compute or inflate expectations. This is a practical step-by-step guide to HPO with no equations or definitions.Item type: Item , Technical Report: Curation Modeling Guideline for the Open Research Knowledge Graph(Hannover : Technische Informationsbibliothek, 2026-07-22) Karras, Oliver; Lorenz, Anna-Lena; Ilangovan, Vinodh; Wittenborg, Tim; Amirmahani, Zahra; John, Lena; Auer, SörenThe Open Research Knowledge Graph (ORKG) enables the structured representation and reuse of scholarly knowledge across scientific domains. As the platform grows, maintaining consistent, interoperable, and semantically meaningful knowledge representations becomes essential. This report presents the first iteration of the ORKG modeling guideline, a set of practical recommendations designed to support contributors and curators in creating high-quality knowledge graph content. The guideline addresses common modeling challenges, including duplicate entities, inconsistent naming, incomplete metadata, and unstructured representations. It introduces 13 recommendations organized into four categories: Reuse Quality, Content Quality, Modeling Quality, and Representation Quality. Each recommendation provides practical guidance on how to apply the principle, its expected impact, and the rationale behind it. Developed collaboratively by the ORKG Curation and Community Building (C&CB) team, the guideline serves as a living resource that will evolve with emerging modeling needs and community experience. By promoting consistent and reusable modeling practices, it aims to improve the quality, sustainability, and usability of scholarly knowledge representations in the ORKG.Item type: Item , A Practical HPO Cookbook(Hannover : Technische Informationsbibliothek, 2026-07-20) Lindauer, Marius; Wever, MarcelOn the one hand, hyperparameter optimization can be crucial to leverage the full potential of machine learning models and pipelines, even in the age of agentic workflows and foundation models. On the other hand, hyperparameter optimization can be quite frustrating for new users because tools suggest very simple use, but in practice, we need to pay attention to sound workflow to ensure reliable results that actually pay off and do not waste compute or inflate expectations. This is a practical step-by-step guide to hyperparameter optimization with no equations or definitions.Item type: Item , Leveraging Large Language Models for Information Extraction in Project Risk Management(Hannover : Technische Informationsbibliothek, 2025) Guggenberger, Tobias; Paetzold, Felix; Protschky, Dominik; Strüker, Jens; Kuhmann, Jochen; Petri, Markus RudolfEffective risk management is crucial but challenging in modern projects due to inherent complexities and the dynamic emergence of risks within informal, unstructured data sources. Traditional approaches often fail to proactively identify risks, creating significant detection gaps. This paper introduces a novel architecture leveraging Large Language Models (LLMs) tailored explicitly to address information extraction (IE) in project risk management (PRM). Using a Design Science Research (DSR) approach, we develop and evaluate an architecture that integrates diverse unstructured data, facilitating continuous, proactive, and context-aware risk identification. The proposed architecture incorporates aggregation, orchestration, and specialized risk agents, allowing for nuanced, timely extraction and structuring of risk indicators. Through iterative development and expert validation, our artifact demonstrates substantial potential to enhance proactive risk management, bridging critical gaps between informal risk emergence and formal identification processes.Item type: Item , Potenzial von Künstlicher Intelligenz für den Einsatz im Projektrisikomanagement : Umfrage im Rahmen des Projekts »KIPRM« (Förderkennzeichen: 01IS22056D)(Hannover : Technische Informationsbibliothek, 2026) Guggenberger, Tobias; Paetzold, Felix; Protschky, Dominik; Petri, Rudolf Markus; Kuhmann, JochenObwohl dem Projektrisikomanagement eine hohe Relevanz zugemessen wird, sind viele Unternehmen mit ihren aktuellen Prozessen unzufrieden. Ein Hauptgrund dafür ist, dass kritische Risikoinformationen oft unstrukturiert in E-Mails, Dokumenten und Protokollen verborgen und nur mit hohem Aufwand zu analysieren sind. Künstliche Intelligenz verspricht hier, Risiken durch die automatisierte Analyse dieser Daten frühzeitiger und zuverlässiger zu erkennen. Unsere Studie zeigt, vor welchen Herausforderungen Projektmanager heute stehen und welches konkrete Potenzial sie dem Einsatz von KI beimessen.Item type: Item , Secure Integration of 5G in Industrial Networks: State of the Art, Challenges and Opportunities(Amsterdam [u.a.] : Elsevier Science, 2024) Michaelides, Sotiris; Lenz, Stefan; Vogt, Thomas; Henze, MartinThe industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.Item type: Item , Assessing the Latency of Network Layer Security in 5G Networks(New York, NY, USA : Association for Computing Machinery, 2025) Michaelides, Sotiris; Mucke, Jonathan; Henze, MartinIn contrast to its predecessors, 5G supports a wide range of commercial, industrial, and critical infrastructure scenarios. One key feature of 5G, ultra-reliable low latency communication, is particularly appealing to such scenarios for its real-time capabilities. However, 5G’s enhanced security, mostly realized through optional security controls, imposes additional overhead on the network performance, potentially hindering its real-time capabilities. To better assess this impact and guide operators in choosing between different options, we measure the latency overhead of IPsec when applied over the N3 and the service-based interfaces to protect user and control plane data, respectively. Furthermore, we evaluate whether WireGuard constitutes an alternative to reduce this overhead. Our findings show that IPsec, if configured correctly, has minimal latency impact and thus is a prime candidate to secure real-time critical scenarios.Item type: Item , Comparing different search methods for the open access journal recommendation tool B!SON(Berlin ; Heidelberg ; New York : Springer, 2023) Entrup, Elias; Eppelin, Anita; Ewerth, Ralph; Hartwig, Josephine; Tullney, Marco; Wohlgemuth, Michael; Hoppe, AnettFinding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.Item type: Item , Az ápolási készségek újra definiálása az AI és a robotizálás terén, kiemelt jelentőséggel a tartós ápolást igénylő állapotokra(Nyíregyháza : University of Debrecen Faculty of Health Department of Gerontology, 2022) Szőllősi, Anna; Kismihók, Gábor; Keszler, Ádám; Karamánné Pakai, Annamária; Lukács, Miklós; Szatmári, Angelika; Ujváriné Siket, AdriennOwing to the enormous improvements in health and lifestyle over the last century, the average age has increased. Although longevity is an important achievement of the modern age, it is a challenge for the care of an ageing population. As people in the richest parts of the world live longer, there is a growing shortage of carers for an ageing population. This paper reviews the literature and describes the global challenges of caregiving, future issues in elderly care, the emergence of robotization in the field of nursing care and how this can contribute to improving the quality of care for the older people. It also discusses the experience of using robots in international and domestic elderly care and briefly describes how the use of AI-based technology has contributed to improving the effectiveness of care in the context of the coronavirus epidemic. The paper concludes by presenting a vision and directions for training development for Advance Practice Nurses, Register Nurses and post-secondary nurses, and other health care professionals to improve attitudes, enhance knowledge, and develop services to improve elderly care.Item type: Item , Digitalisierung in den Gesundheitsberufen([Leverkusen] : Verlag Barbara Budrich, 2024) Weyland, Ulrike; Koschel, Wilhelm; Reiber, Karin; Dorin, Lena; Peters, Miriam; Arndt, Laura; Behr, Dominik; Bergmann, Dana; Buchmann, Ulrike; Ebbighausen, Marc; Engl, Anna-Teresa; Ettl, Katrin; Fathi, Madjid; Fischer, Andreas; Freese, Christiane; Haussmann, Andreas; Hiestand, Stefanie; Hofstetter, Sebastian; Hüttner, Aneli; Jahn, Patrick; Jürgensen, Anke; Kaiser, Sophie; Kaufhold, Marisa; Kismihók, Gábor; Klus, Christina; Kobus, Julia; Köhler, Sonja; Kraft, Bernhard; Makowsky, Katja; Meng, Michael; Michel, Natalie; Nagel, Lisa; Nauerth, Annette; Nerdel, Claudia; Paulicke, Denny; Preißler, Ronja; Rasheed, Hasan A.; Rechl, Friederike; Richter, Katja E.; Richter, Patrick; Schröder, Martina; Schröer, Laura; Schwarz, Karsten; Seltrecht, Astrid; Steindorff, Jenny-Victoria; Stirner, Alexander; Stoevesandt, Dietrich; Völz, Silke; Wagner-Herrbach, Cornelia; Weber, Christian; Wittmann, Eveline; Zepelin, Lyn Anne von; Ziegler, Sven; Zilezinski, MaxDigitale Technologien führen zu veränderten Kommunikations-, Lern- und Arbeitsformen. Für die Gesundheitsberufe ergeben sich durch die Digitalisierung vielfältige Veränderungen und Herausforderungen, die bei positiver Wendung auch als Chance verstanden werden können. Wenn Digitalisierungsprozesse in den Gesundheitsberufen aktiv durch die Berufsgruppen mitgestaltet werden, so können positive Ansätze für die Versorgung hilfs- und pflegebedürftiger Menschen entwickelt werden, aber ebenso für die Professionalisierung der Fachkräfte und des beruflichen Bildungspersonals. Dieser Band dokumentiert die Beiträge zum AG-BFN-Forum „Digitalisierung in den Gesundheitsberufen“, das im Oktober 2021 an der Universität Münster stattfand. Im Fokus stehen aktuelle Entwicklungen in den Bereichen Digitalität in pflege- und gesundheitsberuflichen Handlungsfeldern, Professionalisierung des Bildungspersonals und digital gestützte Lehr-/Lernszenarien in den Gesundheitsberufen.Item type: Item , Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study(Toronto : [Verlag nicht ermittelbar], 2022) Benítez-Andrades, José Alberto; Alija-Pérez, José-Manuel; Vidal, Maria-Esther; Pastor-Vargas, Rafael; García-Ordás, María TeresaBackground: Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.Item type: Item , An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study(Basel : MDPI, 2022) Torrente, María; Sousa, Pedro A.; Hernández, Roberto; Blanco, Mariola; Calvo, Virginia; Collazo, Ana; Guerreiro, Gracinda R.; Núñez, Beatriz; Pimentao, Joao; Sánchez, Juan Cristóbal; Campos, Manuel; Costabello, Luca; Novacek, Vit; Menasalvas, Ernestina; Vidal, María Esther; Provencio, MarianoBackground: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.Item type: Item , Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata(Lausanne : Frontiers Media, 2021) Turki, Houcemeddine; Hadj Taieb, Mohamed Ali; Ben Aouicha, Mohamed; Fraumann, Grischa; Hauschke, Christian; Heller, Lambert[No abstract available]Item type: Item , Food information engineering(Menlo Park, Calif. : AAAI, 2024) Jiomekong, Azanzi; Oelen, Allard; Auer, Sören; Lorenz, Anna-Lena; Vogt, LarsFood information engineering relies on statistical and AI techniques (e.g., symbolic, connectionist, and neurosymbolic AI) for collecting, storing, processing, diffusing, and putting food information in a form exploitable by humans and machines. Food information is collected manually and automatically. Once collected, food information is organized using tabular data representation schema, symbolic, connectionist or neurosymbolic AI techniques. Once collected, processed, and stored, food information is diffused to different stakeholders using appropriate formats. Even if neurosymbolic AI has shown promising results in many domains, we found that this approach is rarely used in the domain of food information engineering. This paper aims to serve as a good reference for food information engineering researchers. Unlike existing reviews on the subject, we cover all the aspects of food information engineering and we linked the paper to online resources built using Open Research Knowledge Graph. These resources are composed of templates, comparison tables of research contributions and smart reviews. All these resources are organized in the “Food Information Engineering” observatory and will be continually updated with new research contributions.Item type: Item , Final Report for the Emmy Noether Project : ConcSys: Reliable and Efficient Complex, Concurrent Software Systems(Hannover : Technische Informationsbibliothek, 2025-06) Pradel, MichaelThe ConcSys project aims to develop techniques for testing and analyzing complex software systems, with a focus on increasing the correctness and performance of such systems. The project was running from March 2015 until December 2024. In this period, we made significant progress, both in terms of scientific results and in terms of building up a research group. The scientific results include novel techniques for (i) finding and preventing concurrency bugs, (ii) understanding and analyzing software performance, (iii) automated test generation, (iv) program analysis for WebAssembly, and (v) foundations of dynamic analysis. These results are presented in 83 peer-reviewed publications at top-tier conferences and journals in software engineering and programming languages, e.g., ICSE, OOPSLA, PLDI, and FSE. Beyond these scientific results, the project has enabled the PI, Michael Pradel, to build up his own a research group, to establish himself as an internationally recognized leader in the field, and to secure a permanent professorship at the University of Stuttgart. The project has directly and indirectly contributed to the careers of 12 doctoral students, out of which seven have been partially funded by the project and six have already graduated.Item type: Item , Final Report on DFG Project "Automatic Transcription of Conversations"(Hannover : Technische Informationsbibliothek, 2025) Häb-Umbach, Reinhold; Schlüter, RalfMulti-talker conversational speech recognition is concerned with transcribing meetings recorded with distant microphones. The difficulty of the task can be attributed to three factors. First, the recording conditions are challenging: The speech signal captured by microphones from a distance is noisy and reverberated and often contains nonstationary acoustic distortions, which makes it hard to decode. Second, there is a significant percentage of time with overlapped speech, where multiple speakers talk at the same time. Finally, the interaction dynamics of the scenario are challenging because speakers articulate themselves in an intermittent manner with alternating segments of speech inactivity, single-, and multi-talker speech. This project was concerned with developing a transcription system that can operate on arbitrarily long input, correctly handles segments of overlapped as well as non-overlapped speech, and transcribes the speech of different speakers consistently into separate output streams. Such a multi-talker Automatic Speech Recognition (ASR) system typically consists of the following three components: a source separation and enhancement block, a diarization stage, that attributes segments of input speech to speakers, and an ASR stage, whereby different orders of processing have been proposed. Those orders differ in when to do diarization. While existing approaches employed separately trained subsystems for diarization, separation, and recognition, our research hypothesis was that a joint approach, which is optimized under a single training objective, should lead to superior solutions compared to the separate optimization of individual components. Such a coherent formulation, however, would not necessarily mean that the three aforementioned tasks had to be carried out in a single, monolithic (probably neural) integrated system. Indeed, the research carried out showed that it is beneficial to have separate subsystems, however, with a tight coupling between them. Examples of such systems we developed are • TS-SEP, which carries out diarization and separation/enhancement, with a tight coupling in-between. • Mixture encoder, which leverages explicit speech separation, but also forwards the not yet separated speech to the ASR module to mitigate error propagation from the separator to the recognizer. • Joint diarization and separation, realized by a statistical mixture model, which integrates a mixture model for diarization and one for separation, that share a common hidden state variable. • Transcription-supported diarization, which uses sentence- and word-level boundaries of the ASR module to support speaker turn detection. Furthermore, we developed new approaches to the individual subsystems and shared several tools and data sets with the research community.Item type: Item , DFG Final Report for Automatic Fact Checking for Biomedical Information in Social Media and Scientific Literature (FIBISS), project number 667374(Hannover : Technische Informationsbibliothek, 2025-04-10) Klinger, Roman; Wührl, AmelieResearch into methods for the automatic verification of facts, i.e., computational models that can distinguish correct information from misinformation or disinformation, is largely focused on the news domain and on the analysis of posts in social media. Among other things, texts are checked for their truthfulness. This can be done by analyzing linguistic features that suggest an intention to deceive or by comparing them with other sources that make comparable statements in terms of content. Most studies focus on politically relevant areas. The biomedical domain is also an area of particular social relevance. In social media, various actors and medical laypersons share reports on treatment methods, successes and failures, such as the (disproven) method of treating viral infections with deworming agents or disinfectants. There are also reports on (disproven) links between treatments and adverse effects, such as the causation of autism by vaccination. However, the biomedical domain, unlike other areas relevant for automated fact checking, benefits from a large resource of reliable scientific articles. The aim of the FIBISS project was therefore to develop and evaluate methods that can extract biomedical claims in social media and compare them with reliable sources. One challenge here is that social media does not typically use technical language, so different vocabularies have to be combined. The approach in FIBISS was therefore to develop generalizing information extraction methods. In the course of the project, large language models also became prominent as a further methodological approach. The project was therefore adapted to optimize general representations of claims in such a way that they are suitable for comparison using automatic fact-checking procedures. As a result, we contribute text corpora that are used to develop and evaluate automated biomedical fact-checking systems. We propose methods that automatically reformulate claims so that they are suitable to be automatically verified. Furthermore, we present approaches that can automatically assess the credibility of claims, even independently of existing evidence.Item type: Item , Final Report of the DFG Project "Drawing Graphs: Geometric Aspects Beyond Planarity" (project number 654838)(Hannover : Technische Informationsbibliothek, 2025-04) Wolff, AlexanderThe aim of our project was to get a better understanding of the mathematical structures that correspond to the different ways of measuring the visual complexity of a drawing of a graph. Examples for such measures are the local crossing number, that is, the maximum number of crossings per edge, the slope number, that is, the number of different slopes in a crossing-free straight-line drawing, the segment number or the line cover number, that is, the number of straight-line segments or straight lines needed to cover a crossing-free straight-line drawing. For a graph, the measures are defined as the minimum over all drawings (of the corresponding type). The center of our studies became the measure segment number, which is known to be NP-hard to compute. In particular, we showed that there is a parameterized algorithm for computing the segment number of a given graph with respect to the several parameters; the natural parameter, the line cover number, and the vertex cover number. The latter proof was the technically most challenging. In a different work, we showed that it is ETR-complete to compute the segment number of a given graph, that is, the segment number of a graph can be expressed in terms of the existential theory of the reals, but its computation is at least as hard as every problem in the complexity class ETR. Moreover, we extended a result concerning the segment number of triconnected cu- bic planar graphs by showing that the segment number of every triconnected 4-regular planar graph with n vertices is at most n + 3, which is tight up to the additive constant. We have proved the first linear universal lower bounds for the segment number of out- erpaths, maximal outerplanar graphs, 2-trees, and planar 3-trees. This shows that the existing algorithms for these graph classes are in fact constant-factor approximation algorithms. For maximal outerpaths, our universal lower bound is best possible.Item type: Item , Implementation of an adaptive BDF2 formula and comparison with the MATLAB Ode15s(Amsterdam [u.a.] : Elsevier, 2014) Celaya, E. Alberdi; Aguirrezabala, J. J. Anza; Chatzipantelidis, P.After applying the Finite Element Method (FEM) to the diffusion-type and wave-type Partial Differential Equations (PDEs), a first order and a second order Ordinary Differential Equation (ODE) systems are obtained respectively. These ODE systems usually present high stiffness, so numerical methods with good stability properties are required in their resolution. MATLAB offers a set of open source adaptive step functions for solving ODEs. One of these functions is the ode15s recommended to solve stiff problems and which is based on the Backward Differentiation Formulae (BDF). We describe the error estimation and the step size control implemented in this function. The ode15s is a variable order algorithm, and even though it has an adaptive step size implementation, the advancing formula and the local error estimation that uses correspond to the constant step size formula. We have focused on the second order accurate and unconditionally stable BDF (BDF2) and we have implemented a real adaptive step size BDF2 algorithm using the same strategy as the BDF2 implemented in the ode15s, resulting the new algorithm more efficient than the one implemented in MATLAB. © The Authors. Published by Elsevier B.V.Item type: Item , Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods(Amsterdam [u.a.] : Elsevier Science, 1995) Hughes, Thomas J. R.An approach is developed for deriving variational methods capable of representing multiscale phenomena. The ideas are first illustrated on the exterior problem for the Helmholtz equation. This leads to the well-known Dirichlet-to-Neumann formulation. Next, a class of subgrid scale models is developed and the relationships to 'bubble function' methods and stabilized methods are established. It is shown that both the latter methods are approximate subgrid scale models. The identification for stabilized methods leads to an analytical formula for τ, the 'intrinsic time scale', whose origins have been a mystery heretofore. © 1995.