Das Kontruktionsproblem

Wir haben in dieser Arbeit das Konstruktionsproblem formuliert und eine nu- merische und eine symbolische LÄosung dieses Problems vorgestellt. Die nume- rische Lösung, die so genannte rückwirkende Dynamik, ermöglicht es, auf einer interaktiven Zeichenoberfläche alle Punkte - sowohl Ursprungspunkte der Kon- struktion als auch abhängige Punkte - frei zu bewegen und die resultierende Bewegung der übrigen Punkte zu beobachten. Cedric ist unseres Wissens das erste dynamische Geometriesystem, das eine solche rückwirkende Dynamik an- bietet. Für die exakte Lösung haben wir einen Algorithmus vorgestellt, der isolierte Nullstellen eines Gleichungssystems findet, die durch Quadratwurzeln ausdrückbar sind. Das Gleichungssystem kann dabei selbst Quadratwurzeln enthalten. Wir haben den Euklidischen Körper vorgestellt und einen Darstellungssatz formuliert, der uns eine minimale Repräsentation in der Anzahl verschiedener Wurzeln von Elementen des Euklidischen KÄorpers erlaubt. Wir haben für diese Darstellung ein Eindeutigkeitsresultat für die meisten der Elemente dieses Körpers gezeigt. Zur Lösung des Gleichungssystems haben wir drei klassische Methoden angepasst und eine weitere, neue Methode eingeführt, um das Gleichungssystem auf die Lösung eines univariaten Polynoms zurückzuführen. Für die Lösung des univariaten Problems haben wir einen Algorithmus vorgestellt, der über dem Grundkörper nur die Faktorisierung benötigt und dann alle durch Quadratwurzeln ausdrückbaren Nullstellen eines vorgegebenen Polynoms finden kann. Wir haben gezeigt, dass dieser Algorithmus bei polynomieller Faktorisierung für Polynome vom Grad d maximal O(dlog (d)) Schritte benötigt und im Regelfall - bei vollständigen Elementen - sogar nur d Faktorisierungen eines Polynoms vom Grad kleiner oder gleich d2 benötigt, also in polynomieller Zeit läuft. Bei exponentieller Faktorisierung ist die Laufzeit in jedem Fall durch diesen Anteil dominiert.Liegt nicht vor

Effects of Youth Mentoring on Depressive Symptoms of Single Mothers

Past evaluation studies of youth mentoring programs have focused solely on the children. While they are often the main recipient of the mentoring, effects on parents should not be neglected. Especially single mothers often face many challenges in their everyday life and might benefit from youth mentoring programs. In the present study we investigate whether youth mentoring programs can lower depressive symptoms in single mothers. The hypothesis was investigated using data of the youth mentoring program” biffy Berlin e.V. Big Friends for Youngsters”. The results showed a significant association between depressive symptoms and duration of the mentoring relationship while relevant covariates were controlled. In a follow-up analysis we explored whether reduced levels of stress might mediate the association and the data was in line with this idea. Implications for future studies are discussed

Effects of Youth Mentoring on Depressive Symptoms of Single Mothers

Past evaluation studies of youth mentoring programs have focused solely on the children. While they are often the main recipient of the mentoring, effects on parents should not be neglected. Especially single mothers often face many challenges in their everyday life and might benefit from youth mentoring programs. In the present study we investigate whether youth mentoring programs can lower depressive symptoms in single mothers. The hypothesis was investigated using data of the youth mentoring program” biffy Berlin e.V. Big Friends for Youngsters”. The results showed a significant association between depressive symptoms and duration of the mentoring relationship while relevant covariates were controlled. In a follow-up analysis we explored whether reduced levels of stress might mediate the association and the data was in line with this idea. Implications for future studies are discussed

Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings

IntroductionEmotional recognition from audio recordings is a rapidly advancing field, with significant implications for artificial intelligence and human-computer interaction. This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition technologies.MethodsWe utilized 1,510 unique audio samples from two databases in German and English to train our models. We extracted various features for emotion prediction, employing Deep Neural Networks (DNN) for general feature analysis, Convolutional Neural Networks (CNN) for spectrogram analysis, and a hybrid model combining both approaches (C-DNN). The study addressed challenges associated with dataset heterogeneity, language differences, and the complexities of audio sample trimming.ResultsOur models demonstrated accuracy significantly surpassing random guessing, aligning closely with human evaluative benchmarks. This indicates the effectiveness of our approach in recognizing emotional states from brief audio clips.DiscussionDespite the challenges of integrating diverse datasets and managing short audio samples, our findings suggest considerable potential for this methodology in real-time emotion detection from continuous speech. This could contribute to improving the emotional intelligence of AI and its applications in various areas

Selection of high-imagery words for the study of episodic memory from middle childhood to old age

The goal of the present study was to select a set of highly imaginable and concrete words that can be used in age-comparable memory research. The selection process included two steps. First, 10 children aged 7-9 years rated 400 high-imagery, concrete, and meaningful words selected from an existing corpus of 1082 spoken words (Singer et al., 2003) on a three-point scale of comprehensibility. Second, two independent raters further selected words to reduce the likelihood of lexical error during recall. As a result, 413 words were retained as stimulus materials for age-comparative investigations of episodic memory performance

Human Aging Magnifies Genetic Effects on Executive Functioning and Working Memory

We demonstrate that common genetic polymorphisms contribute to the increasing heterogeneity of cognitive functioning in old age. We assess two common Val/Met polymorphisms, one affecting the Catechol-O-Methyltransferase (COMT) enzyme, which degrades dopamine (DA) in prefrontal cortex (PFC), and the other influencing the brain-derived neurotrophic factor (BDNF) protein. In two tasks (Wisconsin Card Sorting and spatial working memory), we find that effects of COMT genotype on cognitive performance are magnified in old age and modulated by BDNF genotype. Older COMT Val homozygotes showed particularly low levels of performance if they were also BDNF Met carriers. The age-associated magnification of COMT gene effects provides novel information on the inverted U-shaped relation linking dopaminergic neuromodulation in PFC to cognitive performance. The modulation of COMT effects by BDNF extends recent evidence of close interactions between frontal and medial-temporal circuitries in executive functioning and working memory

Relational mobility predicts social behaviors in 39 countries and is tied to historical farming and threat

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Electrophysiological correlates of selective attention: A lifespan comparison

<p>Abstract</p> <p>Background</p> <p>To study how event-related brain potentials (ERPs) and underlying cortical mechanisms of selective attention change from childhood to old age, we investigated lifespan age differences in ERPs during an auditory oddball task in four age groups including 24 younger children (9–10 years), 28 older children (11–12 years), 31 younger adults (18–25), and 28 older adults (63–74 years). In the Unattend condition, participants were asked to simply listen to the tones. In the Attend condition, participants were asked to count the deviant stimuli. Five primary ERP components (N1, P2, N2, P3 and N3) were extracted for deviant stimuli under Attend conditions for lifespan comparison. Furthermore, Mismatch Negativity (MMN) and Late Discriminative Negativity (LDN) were computed as difference waves between deviant and standard tones, whereas Early and Late Processing Negativity (EPN and LPN) were calculated as difference waves between tones processed under Attend and Unattend conditions. These four secondary ERP-derived measures were taken as indicators for change detection (MMN and LDN) and selective attention (EPN and LPN), respectively. To examine lifespan age differences, the derived difference-wave components for attended (MMN and LDN) and deviant (EPN and LPN) stimuli were specifically compared across the four age groups.</p> <p>Results</p> <p>Both primary and secondary ERP components showed age-related differences in peak amplitude, peak latency, and topological distribution. The P2 amplitude was higher in adults compared to children, whereas N2 showed the opposite effect. P3 peak amplitude was higher in older children and younger adults than in older adults. The amplitudes of N3, LDN, and LPN were higher in older children compared with both of the adult groups. In addition, both P3 and N3 peak latencies were significantly longer in older than in younger adults. Interestingly, in the young adult sample P3 peak amplitude correlated positively and P3 peak latency correlated negatively with performance in the Identical Picture test, a marker measure of fluid intelligence.</p> <p>Conclusion</p> <p>The present findings suggest that patterns of event-related brain potentials are highly malleable within individuals and undergo profound reorganization from childhood to adulthood and old age.</p

Adaptive Block Testing

This article introduces Adaptive Block Testing (ABT), a method to test N units for a binary variable with known baseline probability pi for each unit, assuming that a test is available which may take arbitrary number of units and tests negative if all units are negative, and positive otherwise. A proof is given that the current method is optimal up to rounding. ABT is applicable to screen a large population of patients for the presence of the RNA of a virus, as for example the SARS-CoV-2, using block testing by polymerase chain reactions. ABT uses the block tests and adaptively chooses from the pool participants such that the entropy gain in each test is maximized. For a baseline probability of 1% of the tested patients to be sick, the method needs 2.4 times less tests than a block testing method with a block size of 10, the optimal block size for a standard block test at a baseline probability of 1%