The precariousness of the health situation in England from the 14th century onwards led to the use of any curative means, whether scientific, religious or ritual-magical. In this context, healing charms were seen as accessible and practicable methods. They were often added to medical prescriptions and herbal remedies in medical or pseudo-pharmacological compilations, as they were considered an alternative form of therapy equally valid in the treatment of ailments. Many charms created during this period were a mixture of magic, religion and folklore, but some received new cultural stimulus, by incorporating original elements and symbolism from Arabic, Greek and Hebrew magical texts introduced to the island. This work will focus on a selection of 15th century healing charms. The analysis aims to demonstrate that the principles of the new occult and esoteric doctrines, circulating in the intellectual and cultural centers of the island, influenced the magical healing ritual. The study of pseudo-Solomonic texts, although strongly censored by the Church, however contributed to the creation of new textual amulets, which were used in addition to the pre-existing charms so becoming a further alternative medium in the therapeutic procedure.
<i>Dies Buch gehört dem König</i> (<i>This Book Belongs to the King</i>), written and published in 1843 by the German Romantic author Bettina von Arnim, is a quasi-open letter, presented as a series of fictional dialogues with traces of a novel. Dedicated to the newly crowned King of Prussia, Friedrich Wilhelm IV, the letter unfolds social grievances and aims to persuade Friedrich Wilhelm to act like a just king. Due to its delicate socio-critical impetus, the letter does so through strategies of obfuscation and by using a richly pictorial, seemingly naive and lavish way of speech rather than taking an openly reproachful stance. Crucially, von Arnim does not install herself as the letter’s speaker but instead fictionalizes the letter and presents Goethe’s mother, Catharina Elisabeth Goethe, as the letter’s primary voice (‘Frau Rat’). By using a well-respected figure of the ruling class as the letter’s main voice, von Arnim aimed at minimizing its scandalous potential. But even prior to publishing the letter, von Arnim had already managed to trick Friedrich Wilhelm and the Prussian censors herself: by fusing the book’s title and dedication, she paratextually outwitted both the censors and the King, whose permission she sought precisely to bypass Prussian censorship. This article shows how von Arnim managed to avoid a larger scandal both textually by implementing semi-fictional devices and paratextually by presenting the letter as an affirmation of Friedrich Wilhelm IV and his policies.
History of scholarship and learning. The humanities
Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open-weight VLMs on factual knowledge in the German and English language. We disentangle the image-related aspects from the textual ones by analyzing accu-racy with jury-as-a-judge in both prompt languages and images from German and international contexts. We found that for celebrities and sights, VLMs struggle because they are lacking visual cognition of German image contents. For animals and plants, the tested models can often correctly identify the image contents ac-cording to the scientific name or English common name but fail in German lan-guage. Cars and supermarket products were identified equally well in English and German images across both prompt languages.
This paper introduces a novel benchmark dataset designed to evaluate the capabilities of Vision Language Models (VLMs) on tasks that combine visual reasoning with subject-specific background knowledge in the German language. In contrast to widely used English-language benchmarks that often rely on artificially difficult or decontextualized problems, this dataset draws from real middle school curricula across nine domains including mathematics, history, biology, and religion. The benchmark includes over 2,000 open-ended questions grounded in 486 images, ensuring that models must integrate visual interpretation with factual reasoning rather than rely on superficial textual cues. We evaluate thirteen state-of-the-art open-weight VLMs across multiple dimensions, including domain-specific accuracy and performance on adversarial crafted questions. Our findings reveal that even the strongest models achieve less than 45% overall accuracy, with particularly poor performance in music, mathematics, and adversarial settings. Furthermore, the results indicate significant discrepancies between success on popular benchmarks and real-world multimodal understanding. We conclude that middle school-level tasks offer a meaningful and underutilized avenue for stress-testing VLMs, especially in non-English contexts. The dataset and evaluation protocol serve as a rigorous testbed to better understand and improve the visual and linguistic reasoning capabilities of future AI systems.
Manuel Lardelli, Giuseppe Attanasio, Anne Lauscher
The translation of gender-neutral person-referring terms (e.g., the students) is often non-trivial. Translating from English into German poses an interesting case -- in German, person-referring nouns are usually gender-specific, and if the gender of the referent(s) is unknown or diverse, the generic masculine (die Studenten (m.)) is commonly used. This solution, however, reduces the visibility of other genders, such as women and non-binary people. To counteract gender discrimination, a societal movement towards using gender-fair language exists (e.g., by adopting neosystems). However, gender-fair German is currently barely supported in machine translation (MT), requiring post-editing or manual translations. We address this research gap by studying gender-fair language in English-to-German MT. Concretely, we enrich a community-created gender-fair language dictionary and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. Using these novel resources, we conduct the first benchmark study involving two commercial systems and six neural MT models for translating words in isolation and natural contexts across two domains. Our findings show that most systems produce mainly masculine forms and rarely gender-neutral variants, highlighting the need for future research. We release code and data at https://github.com/g8a9/building-bridges-gender-fair-german-mt.
Purpose: Compare of the sustainability transportation systems between Indonesia and Germany. Learning implements some lessons learned from the German experience to the development of transportation planning in Indonesia to support sustainable land use and transportation by comparing and analyzing the probability of converting and adopting reducing private transportation, landscape, financing mechanisms, and transport mechanism between Germany and Indonesia.
Design/methodology/approach: Analyzing obtained data based on the comparison's parameters framework.
Findings: Potentially, a parameter that can be set to develop a better transportation system for environmental and people safety, or even to reduce the congestion that occurs in most of Indonesia's metropolitan areas.
Research limitations/implications: Since the study location is in fact somewhat distant, the information that is gathered to analyze the comparison is only chosen from each country's broad outline and is not otherwise specified.
Practical implications: Finding solution for a better sustainable transportation in Indonesia that can be implemented.
Originality/value: The analytical framework that is implemented in this comparative study is a methodically innovative approach form of developing an Innovative Transport System that is to aim a better use of transport network.
Paper type: Literature Review
In dem Beitrag werden Ergebnisse aus zwei Studien vorgestellt, in denen Deutschlehrkräfte und ihre Vertrautheit mit, ihre Nutzung von und ihre Einstellung zu digitalen Medien untersucht worden sind. In der eigenfinanzierten DIDID-Studie („Digitales Distanzlernen im Deutschunterricht während der Corona-Pandemie“), an der von November 2020 bis März 2021 auf Basis eines Online-Fragebogens 1981 Deutschlehrkräfte teilgenommen haben, sind Daten zu digitalen Kompetenz-, Einstellungs- und Nutzungsmustern von Deutschlehrkräften in Bezug auf digitale Medien während der Corona-Pandemie gewonnen worden. In der von der DFG 2021/22 geförderten GETDIME-Studie (“Effects of the Covid Pandemic on German Language and Literature Teachers and their Familiarity with, Use of and Views on Digital Media”) wurden auf Basis unveröffentlichter Daten in der ICILS-Studie 2018 digitale Kompetenz-, Einstellungs- und Nutzungsmuster von Deutschlehrkräften vor der Corona-Pandemie untersucht. Die Extrapolation von Grundprofilen der Deutschlehrkräfte in DIDID und ICILS ermöglichte einen unmittelbaren Vergleich. Dieser lässt eine leichte Progression der in DIDID untersuchten Deutschlehrkräfte in ihrer Vertrautheit mit, Nutzung von und Einstellung zu digitalen Medien während der Corona-Pandemie gegenüber den in ICILS 2018 teilnehmenden Deutschlehrkräften vor der Pandemie erkennbar werden. Dabei zeigt sich ein deutlich erhöhtes Fortbildungsinteresse und ein klarer konturierter Fortbildungsbedarf.
Abstract (english): German teachers and their familiarity with, use of, and attitutes toward digital media during and before the Corona pandemic in Germany. Finding from two studies.
This paper presents results from two studies that investigated German teachers and their familiarity with, use of, and attitudes towards digital media. In the self-funded DIDID study („Digital Distance Learning in the German Classroom during the Corona Pandemic“), in which 1981 German teachers participated from November 2020 to March 2021 on the basis of an online questionnaire, data on digital competence, attitude and usage patterns of German teachers with regard to digital media during the Corona Pandemic were obtained. In the GETDIME study („Effects of the Covid Pandemic on German Language and Literature Teachers and their Familiarity with, Use of and Views on Digital Media“), funded by the DFG 2021/22, digital competence, attitude and usage patterns of German teachers prior to the Corona Pandemic were investigated on the basis of unpublished data in the ICILS study of 2018. The extrapolation of basic profiles of German teachers in DIDID and ICILS enabled a direct comparison. This reveals a slight progression of the German teachers studied in DIDID in their familiarity with, use of and attitude towards digital media during the Corona pandemic in comparison with the German teachers participating in ICILS 2018 before the pandemic. This reveals a clearly increased interest in further training and a clearly contoured need for further training.
The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files, as well as extracting content from articles. Semantic search queries are used for data retrieval, while text embeddings and summarization using LLMs present succinct literature reviews. Interaction with PDFs is enhanced through a user-friendly graphical user interface (GUI). The suite also features integrated programs for bibliographic organization, interaction and query, and literature review summaries. This tool presents a robust solution to automate and optimize the process of literature review in academic and industrial research.
This study presents German FinBERT, a novel pre-trained German language model tailored for financial textual data. The model is trained through a comprehensive pre-training process, leveraging a substantial corpus comprising financial reports, ad-hoc announcements and news related to German companies. The corpus size is comparable to the data sets commonly used for training standard BERT models. I evaluate the performance of German FinBERT on downstream tasks, specifically sentiment prediction, topic recognition and question answering against generic German language models. My results demonstrate improved performance on finance-specific data, indicating the efficacy of German FinBERT in capturing domain-specific nuances. The presented findings suggest that German FinBERT holds promise as a valuable tool for financial text analysis, potentially benefiting various applications in the financial domain.
This paper surveys the empirical literature of inflation targeting. The main findings from our review are the following: there is robust empirical evidence that larger and more developed countries are more likely to adopt the IT regime; the introduction of this regime is conditional on previous disinflation, greater exchange rate flexibility, central bank independence, and higher level of financial development; the empirical evidence has failed to provide convincing evidence that IT itself may serve as an effective tool for stabilizing inflation expectations and for reducing inflation persistence; the empirical research focused on advanced economies has failed to provide convincing evidence on the beneficial effects of IT on inflation performance, while there is some evidence that the gains from the IT regime may have been more prevalent in the emerging market economies; there is not convincing evidence that IT is associated with either higher output growth or lower output variability; the empirical research suggests that IT may have differential effects on exchange-rate volatility in advanced economies versus EMEs; although the empirical evidence on the impact of IT on fiscal policy is quite limited, it supports the idea that IT indeed improves fiscal discipline; the empirical support to the proposition that IT is associated with lower disinflation costs seems to be rather weak. Therefore, the accumulated empirical literature implies that IT does not produce superior macroeconomic benefits in comparison with the alternative monetary strategies or, at most, they are quite modest.
Mhd. Suhyb Salama, Lazaros Spaias, Kathrin Poser
et al.
It is common in estuarine waters to place fixed monitoring stations, with the advantages of easy maintenance and continuous measurements. These two features make fixed monitoring stations indispensable for understanding the optical complexity of estuarine waters and enable an improved quantification of uncertainties in satellite-derived water quality variables. However, comparing the point-scale measurements of stationary monitoring systems to time-snapshots of satellite pixels suffers from additional uncertainties related to temporal/spatial discrepancies. This research presents a method for validating satellite-derived water quality variables with the continuous measurements of a fixed monitoring station in the Ems Dollard estuary on the Dutch-German borders. The method has two steps; first, similar in-situ measurements are grouped. Second, satellite observations are upscaled to match these point measurements in time and spatial scales. The upscaling approach was based on harmonizing the probability distribution functions of satellite observations and in-situ measurements using the first and second moments. The fixed station provided a continuous record of data on suspended particulate matter (SPM) and chlorophyll-a (Chl-a) concentrations at 1 min intervals for 1 year (2016–2017). Satellite observations were provided by Sentinel-2 (MultiSpectral Instrument, S2-MSI) and Sentinel-3 (Ocean and Land Color Instrument, S3-OLCI) sensors for the same location and time of in-situ measurements. Compared to traditional validation procedures, the proposed method has improved the overall fit and produced valuable information on the ranges of goodness-of-fit measures (slope, intercept, correlation coefficient, and normalized root-mean-square deviation). The correlation coefficient between measured and derived SPM concentrations has improved from 0.16 to 0.52 for S2-MSI and 0.14 to 0.84 for S3-OLCI. For the Chl-a matchup, the improvement was from 0.26 to 0.82 and from 0.14 to 0.63 for S2-MSI and S3-OLCI, respectively. The uncertainty in the derived SPM and Chl-a concentrations was reduced by 30 and 23% for S2-SMI and by 28 and 16% for S3-OLCI. The high correlation and reduced uncertainty signify that the matchup pairs are observing the same fluctuations in the measured variable. These new goodness-of-fit measures correspond to the results of the performed sensitivity analysis, previous literature, and reflect the inherent accuracy of the applied derivation model.
Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger
et al.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0.435.
Hartmut Surmann, Ivana Kruijff-Korbayova, Kevin Daun
et al.
Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.
Anastasiia Usmanova, François Portet, Philippe Lalanda
et al.
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. Current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models sent by the server. Such setting is not realistic in mobile pervasive computing where data storage must be kept low and data characteristic can change dramatically. To account for this variability, a solution is to use the data regularly collected by the client to progressively adapt the received model. But such naive approach exposes clients to the well-known problem of catastrophic forgetting. To address this problem, we have defined a Federated Continual Learning approach which is mainly based on distillation. Our approach allows a better use of resources, eliminating the need to retrain from scratch at the arrival of new data and reducing memory usage by limiting the amount of data to be stored. This proposal has been evaluated in the Human Activity Recognition (HAR) domain and has shown to effectively reduce the catastrophic forgetting effect.
Luiz G. Véras, Anna K. F. Gomes, Guilherme A. R. Dominguez
et al.
Countless traffic accidents often occur because of the inattention of the drivers. Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations, promoting the interest of the scientific community in these last years. Commonly, these solutions identify the lack of attention and alert the driver, in order to help her/him to recover the attention, avoiding serious accidents and preserving lives. Our work presents a Systematic Literature Review (SLR) of the methods and criteria used to detect attention of drivers at the wheel, focusing on those methods based on images. As results, 50 studies were selected from the literature on drivers' attention detection, in which 22 contain solutions in the desired context. The results of SLR can be used as a resource in the preparation of new research projects in drivers' attention detection.
The basis of critical thinking foreign language teaching is to regard language as a means to develop higher-order thinking ability. In language teaching, it aims to develop the thinking ability of learners, and to use the language-learning concept of thinking ability in situations beyond language classrooms. It contains a series of principles that reflect this view of language and language teaching, which can be used to support various classroom-operating procedures. Studies have summarized the principles of critical thinking foreign language teaching into 8 items: target, evaluate, routinize, reflect, inquire, fulfill, integrate, and content. This study takes the literature course that requires the highest critical thinking ability in the teaching of German major in universities as an example, and investigates the influence of the most easily neglected evaluation principle on students' learning motivation through quantitative experiments.
Using a custom-designed database of 388,000 first editions of German Literature this paper investigates the long-term development of genre-indicating subtitles over more than 500 years of literary history. This approach adds a social-institutional perspective to recent work in the field of genre theory, and is a first step towards combining historical testimony, i.e. historical actors’ classifications, and textual features in a single model. Starting from the fundamental question of how many books have generic subtitles, the paper analyses the use of the most common genre labels, the relation between generic subtitles and genre production, periods of the permanent presence of generic terms (institutional cycles) and periods of generic differentiation. It identifies recurrent patterns in the development of generic subtitles using K-Means-Clustering and Dynamic Time Warping (DTW) and sheds light on literature’s changing relation to history and truth, thereby underpinning recent theoretical work on the practices of poetic invention.
This paper examines the Georgian migrant press, which emerged as a result of the flight
of Georgian intellectuals from the Soviet Communist regime. It reviews publications
such as Chveni Drosha (Our Flag), Tavisuphali Sakartvelo (Free Georgia), Bedi
Kartlisa (Destiny of Kartli) and others, which became cultural-intellectual and
national islands beyond ideological frontiers. After the occupation of Georgia, the efforts
of emigrants focused on generating an information war against the Soviet occupation.
The emigrants living in Paris were especially active in this regard. The newspapers and
magazines were mainly issued in Georgian, but also in English, French, and German.
Special attention is paid to the historical, literary and scientific journal Bedi
Kartlisa (Revue de Kartvélologie), founded in Paris, in 1948, by Nino Salia. The
journal brought together Kartvelologists living abroad, and played an important role
in promoting Georgian history, literature, science, and culture. In this magazine,
Georgian and foreign journalists, prominent writers and scientists worked together.
The study aims to examine the pathos and the main topics of the emigrant press, based
on the analysis of a large number of magazines and personal archives of Georgian
emigrants. Their role in keeping the Georgian national soul alive abroad, disclosing the
regime, introducing the Georgian intellectual and spiritual culture to the Europeans
and transferring European ideas to Georgia are among the most relevant findings. The
role of these magazines is invaluable not only in the history of Georgian journalism,
but also in the history of the Georgian national struggle.