University of Konstanz
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What occupation does not tell about : Measuring status in Ukraine's capital
No full textThis study examines how social status is assessed in contemporary Ukrainian society (on the case of Kyiv city). We expect that social status deviates from traditional occupation-based measures prevalent in Western contexts and is more closely related to material wealth than to occupation. Using a multidimensional framework (Warner’s Status Characteristics Index), we employ a factorial survey experiment to test the importance of four (independent) dimensions for status assessment: occupation, income, area of residence, and housing conditions. The experiment was implemented as an online survey in Kyiv 2023. The results confirm that income is the primary determinant of social status judgment, followed by housing conditions, while occupation and area of residence exert less influence. The criteria for judging social status remain consistent across gender, age, and socio-economic strata. This study contributes to the establishment of a standardized index for measuring social status and highlights the nuances of social stratification in Ukraine. Finally, we critically discuss our findings and the future relevance of occupation as a status marker due to the current war and its aftermath.publishe
Qualitätsstandards für forschungsdaten.info
No full textIm Rahmen der Landesinitiative bwFDM hat sich die Redaktion von forschungsdaten.info ein Set an Qualitätsstandards gegeben. Diese werden Stück für Stück in die redaktionelle Arbeit überführt.publishe
A Theory of Jamming and Elastic Instability in Low Temperature Amorphous Solids
No full textDespite intense research, no first-principles theory has yet rationalized the rich
phenomena and vibrational anomalies in amorphous solids. This monograph presents an analytical study, which in takes the first steps towards an exhaustive microscopic theory. The derived microscopic theory successfully describes the jammed and unjammed phase of disordered systems at zero temperature. Employing the Zwanzig-Mori projection operator formalism, we expand the Mode-Coupling Theory to coherently describe the jammed state. We identify a symmetry constraint in the sequence of local scattering events, compelling us to go beyond the standard self-consistent Born approximation: A planar theory does not recover the characteristic Rayleigh-sound attenuation in amorphous solids. Thus, we include non-planar contributions in the theoretical model.
The universal vibrational properties of amorphous solids at low temperatures are
recovered in the jammed phase. We identify a diffusive regime of modes, characterized by a plateau in the vibrational Density of States. Here, the energy suffices to resolve the local disorder. Below the disorder-dominated regime, modes can propagate, and the system can be approximated as an elastic medium. The vanishing of the transverse speed of sound heralds the unjamming instability. The diffusive regime extends down to zero frequency directly at the critical point. In the unjammed phase, the theory predicts no viscous flow
but the presence of modes with zero restoring forces. As a consequence, injected momentum causes plastic rearrangements of stable sub-clusters whose size diverges at the jamming transition. Utilizing the Euclidean Random Matrix model, we support our theory by deriving an equivalent theory in the harmonic approximation of the potential energy. The quantitative predictions of the theory are in good agreement with Philipp Baumgärtel's numerical solution of the scalar Euclidean Random Matrix model published in (Vogel et al. Phys. Rev. X, vol. 15:011.030, 2025).publishe
Gute Betreuung, faire Bewertungen und angemessene Arbeitsbedingungen ermöglichen exzellente Promotionen in der Psychologie
No full textpublishe
Interactive Visual Investigation of Word Embedding Contextualizations in Large Language Models
No full textLanguage models (LMs) have revolutionized natural language processing (NLP) methods with their ability to generate text and perform various language-related tasks. One essential characteristic of LMs is their representation of words through contextual word embeddings, i.e., high dimensional vectors capturing linguistic properties (e.g., semantics and syntax) of their surrounding contexts. Supporting the interpretability of contextual word embeddings is crucial for assessing model strengths and limitations, identifying encoded biases, and making informed decisions about sufficient embedding layers for text-analysis tasks. While word embeddings have proven to be effective representations for various NLP methods, much remains to learn about how they encode and represent language properties. Existing approaches often examine embeddings in relation to high-level tasks such as question answering or sentiment classification, overlooking the detailed analysis of linguistic phenomena, e.g., how models represent word categories like function words. These fine-grained linguistic insights are essential for inspecting whether LMs "understand" language, as sometimes assumed in academic publications. Moreover, given that embeddings have already demonstrated their ability to capture diverse language properties, they can serve as effective features for further analysis tasks when appropriately applied. A deeper examination of their successful utilization is still missing.
In the first part of the thesis, we address the research gap related to linguistically motivated embedding explanation methods. Collaborating with computational linguists, we design three visual analytics techniques that facilitate the exploration of embedding properties. In particular, we investigate reasons for word contextualization, i.e., differences in the embeddings for the same word used in different contexts. We explore whether contextualization captures the word's semantic meaning (and polysemy) or context variations, as assumed by the related work. We design visual methods for gaining insights into linguistic properties encoded in embedding vectors and show how the information gets propagated through the models’ architectures. Lastly, we investigate how models capture word functionality, i.e., whether models correctly encode the meaning and constraints of different function word classes.
In the second part of the thesis, we examine methods on how to utilize embedding vectors for diverse application scenarios in order to assist researchers in selecting the best model for a user and task at hand. In particular, in this thesis, we present three interactive approaches for visually comparing model adaptations and their generated outputs by using embedding vectors as the main features for the analysis. First, we design visual methods that enable effective comparison of text outputs generated by causal LMs, including the biases associated with different prompt inputs. Second, we develop visual methods to compare masked LM adaptations, particularly their influence on semantic language concept representations. Finally, we explore methods that incorporate gamification to learn users' preferences captured through word embedding similarity for an optimal LM selection.
Through the presented visual analytics methods, we show both the LM strengths and limitations and motivate the development of more linguistically aware LMs and further methods for their effective analysis.publishe
Beyond musical training : Individual influences on the perception of the speech-to-song illusion
No full textWhen a spoken phrase is repeated several times, listeners often report a perceptual illusion during which speech is transformed into song. The speech-to-song (STS) illusion is often attributed to prosodic elements of speech, though listeners can vary greatly in their STS experience. While previous research established robust links between music aptitude and STS, the present study asks whether other cognitive traits may also influence STS. Individual (in)sensitivity to nonverbal aspects of speech, specifically speech prosody, has been previously linked to autistic traits and emotional intelligence. We test whether the presence of autistic traits, the level of emotional intelligence and musical training, as well as syntactic complexity influence the likelihood, speed, and strength of STS among native British English listeners. The results provide evidence for the involvement of some but not all studied traits. We found sentence complexity to be interacting with a composite score of musical training, and emotional intelligence for the likelihood of STS, whereas sentence complexity influenced the strength of the transformation. These results suggest that individual listener variability may interact with the linguistic parameters of sentences in STS. Crucially, sensitivity to prosody through emotional intelligence or by the presence of autistic traits does not mediate the transformation.publishe
Single-Electron Spin Qubits in Silicon for Quantum Computing
No full textThe recent decade has witnessed substantial advancements in silicon quantum computing. Important milestones include demonstrations of quantum gates exceeding the fault-tolerance threshold, high-fidelity single-shot spin readout, hot quantum bits (hot qubits), and compact scalable spin arrays. Silicon qubits hold promise to leverage semiconductor industry technologies into scalable qubit manufacturing. Both the academic and industry communities are striving to push this advantage into reality. However, formidable challenges persist in the quest to develop a fully operational universal quantum computer. This review focuses on single-spin qubits in silicon. First, we start with foundational spin qubit theory. Then, we discuss gate-defined quantum dots and donor dot systems, with a particular emphasis on two-qubit gate operations and the scalability of qubit arrays. Lastly, we address long-distance coupling, highlighting key areas for future research and potential scale-up strategies for this rapidly evolving field.publishe
Observation and control of nonmonotonic recoils in a viscoelastic fluid
No full textWe experimentally study the relaxation dynamics of a colloidal particle in a micellar viscoelastic fluid following different driving protocols. When the particle is driven at constant velocity for a finite duration, its recovery to equilibrium is always monotonic. In contrast, altering the driving velocity during the protocol induces nonmonotonic relaxation. Our results are in quantitative agreement with the analytical solution of a minimal micromechanical model exhibiting only two distinct eigenmodes independent of the specific protocol. Notably, the model enables selective suppression of one or both modes—an effect confirmed experimentally. Because the model is broadly applicable to diverse viscoelastic fluids, our findings offer a general framework for tailoring relaxation dynamics in complex environments.publishe
Mechanism of cotranslational protein N-myristoylation in human cells
No full textN-myristoyltransferases (NMTs) cotranslationally transfer the fatty acid myristic acid to the N terminus of newly synthesized proteins, regulating their function and cellular localization. These enzymes are important drug targets for the treatment of cancer and viral infections. N-myristoylation of nascent proteins occurs specifically on N-terminal glycine residues after the excision of the initiator methionine by methionine aminopeptidases (METAPs). How NMTs interact with ribosomes and gain timely and specific access to their substrates remains unknown. Here, we show that human NMT1 exchanges with METAP1 at the ribosomal tunnel exit to form an active cotranslational complex together with the nascent polypeptide-associated complex (NAC). NMT1 binding is sequence selective and specifically triggered by methionine excision, which exposes the N-myristoylation motif in the nascent chain. The revealed mode of interaction of NMT1 with NAC and the methionine-cleaved nascent protein elucidates how a specific subset of proteins can be efficiently N-myristoylated in human cells.publishe