Refine
Year of publication
Document Type
- Article (35976)
- Doctoral Thesis (6520)
- Monograph/Edited Volume (5570)
- Postprint (3296)
- Review (2303)
- Part of a Book (1089)
- Other (969)
- Preprint (569)
- Conference Proceeding (561)
- Part of Periodical (531)
Language
- English (30983)
- German (26158)
- Spanish (363)
- French (330)
- Italian (115)
- Russian (113)
- Multiple languages (70)
- Hebrew (36)
- Portuguese (25)
- Polish (24)
Keywords
- Germany (207)
- climate change (182)
- Deutschland (145)
- machine learning (88)
- European Union (79)
- diffusion (78)
- Sprachtherapie (77)
- Migration (75)
- morphology (74)
- Logopädie (73)
Institute
- Institut für Biochemie und Biologie (5492)
- Institut für Physik und Astronomie (5458)
- Institut für Geowissenschaften (3673)
- Institut für Chemie (3485)
- Wirtschaftswissenschaften (2645)
- Historisches Institut (2526)
- Department Psychologie (2354)
- Institut für Mathematik (2152)
- Institut für Romanistik (2114)
- Sozialwissenschaften (1883)
Ada (Fishman) Maimon
(2023)
Dass vielfältige Inhalte und Meinungen über eine Vielzahl an Medien verbreitet werden, ist für unsere demokratische Gesellschaft heute wichtiger denn je. Gerade deshalb ist es unabdingbar, Meinungsmacht einzelner Medienunternehmen zu verhindern und dadurch zur Meinungsvielfalt beizutragen. Diese bedeutende Aufgabe kommt der Medienkonzentrationskontrolle des Medienstaatsvertrages zu. Doch haben die digitalisierungsbedingten Veränderungen in der Medienlandschaft zu einem inkonsistenten Prüfungsregime der Medienkonzentrationskontrolle geführt, da medienrechtlich aktuell nicht alle für die Meinungsbildung relevanten Medienakteure ausreichend erfasst werden. Die Arbeit untersucht die Thematik im Kontext der nationalen sowie internationalen medien- und wettbewerbsrechtlichen Rahmenbedingungen. Basierend auf den dabei gewonnenen Erkenntnissen wird ein den aktuellen Erfordernissen entsprechender normativer Vorschlag unterbreitet.
Abstract
In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non‐existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics‐based modeling during strong geomagnetic storms. Physics‐based models show a stable performance during periods of disturbed geomagnetic activity if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network‐ and physics‐based models of the plasmasphere in an optimal way by using data assimilation. The proposed approach utilizes advantages of both neural network‐ and physics‐based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in‐situ density measurements from RBSP‐A for an 18‐month out‐of‐sample period from June 30, 2016 to January 01, 2018 and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth's plasmasphere for a number of events in the past, including the Halloween storm in 2003.
In the context of the “postcatastrophic” culture after the Shoah, the question of the ethics of seeing has developed its own specificity and incisiveness, one that resulted from the complex distribution of the visibility and invisibility of the criminal acts themselves.
On the one hand, the perpetrators’ efforts to conceal their crimes and erase their tracks stood in opposition to the desire of the same to meticulously document their crimes. On the other hand, local communities became direct eyewitnesses to the persecution and killing of Jews—in mass shootings as well as in the extermination camps, which were frequently set up close to populated areas.
It is precisely these two aspects—the photographic archives of the perpetrators as well as the bystanders’ eyewitnessing—around which heated debates unfold.
They are also of primary interest to postmemorial art, which grapples with the legacy of this visuality and visibility of the Shoah.
This chapter discusses the possibility of a critical analysis of the images from contaminated photographic material in the Nazi archives in postmemorial art and film as well as artistic projects focussing on the problem of visibility and seeing that deal with the question of the possibility of the witnessing of bystanders and of future generations who are faced with the legacy of the bystander experience.
These projects were developed during the time of intense examination—and not only artistic—of the role of direct eyewitnesses to the Shoah, examinations that were characteristic for the public discourse in Poland after the year 2000.
Gedichte
(2022)
Background: Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.
Method: Here, a systematic literature review on mental state classification for wearable electroencephalog-raphy is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.
Results: Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.
Conclusions: Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.
Background
Many high-income countries are grappling with severe labour shortages in the healthcare sector. Refugees and recent migrants present a potential pool for staff recruitment due to their higher unemployment rates, younger age, and lower average educational attainment compared to the host society's labour force. Despite this, refugees and recent migrants, often possessing limited language skills in the destination country, are frequently excluded from traditional recruitment campaigns conducted solely in the host country’s language. Even those with intermediate language skills may feel excluded, as destination-country language advertisements are perceived as targeting only native speakers. This study experimentally assesses the effectiveness of a recruitment campaign for nursing positions in a German care facility, specifically targeting Arabic and Ukrainian speakers through Facebook advertisements.
Methods
We employ an experimental design (AB test) approximating a randomized controlled trial, utilizing Facebook as the delivery platform. We compare job advertisements for nursing positions in the native languages of Arabic and Ukrainian speakers (treatment) with the same advertisements displayed in German (control) for the same target group in the context of a real recruitment campaign for nursing jobs in Berlin, Germany. Our evaluation includes comparing link click rates, visits to the recruitment website, initiated applications, and completed applications, along with the unit cost of these indicators. We assess statistical significance in group differences using the Chi-squared test.
Results
We find that recruitment efforts in the origin language were 5.6 times (Arabic speakers) and 1.9 times (Ukrainian speakers) more effective in initiating nursing job applications compared to the standard model of German-only advertisements among recent migrants and refugees. Overall, targeting refugees and recent migrants was 2.4 (Ukrainians) and 10.8 (Arabic) times cheaper than targeting the reference group of German speakers indicating higher interest among these groups.
Conclusions
The results underscore the substantial benefits for employers in utilizing targeted recruitment via social media aimed at foreign-language communities within the country. This strategy, which is low-cost and low effort compared to recruiting abroad or investing in digitalization, has the potential for broad applicability in numerous high-income countries with sizable migrant communities. Increased employment rates among underemployed refugee and migrant communities, in turn, contribute to reducing poverty, social exclusion, public expenditure, and foster greater acceptance of newcomers within the receiving society.