julilili42/papers huggingface dataset:
Active Iterative Social Inference in Multi-Trial Signaling Games
2023-01-01

Abstract Human behavioral choices can reveal intrinsic and extrinsic decision-influencing factors. We investigate the inference of choice priors in situations of referential ambiguity. In particular, we use the scenario of signaling games and investigate to which extent study participants profit from actively engaging in the task. Previous work has revealed that speakers are able to infer listeners’ choice priors upon observing ambiguity resolution. However, it was also shown that only a small group of participants was able to strategically construct ambiguous situations to create learning opportunities. This paper sets to address how prior inference unfolds in more complex learning scenarios. In Experiment 1, we examine whether participants accumulate evidence about inferred choice priors across a series of four consecutive trials. Despite the intuitive simplicity of the task, information integration turns out to be only partially successful. Integration errors result from a variety of sources, including transitivity failure and recency bias. In Experiment 2, we investigate how the ability to actively construct learning scenarios affects the success of prior inference and whether the iterative settings improve the ability to choose utterances strategically. The results suggest that full task engagement and explicit access to the reasoning pipeline facilitates the invocation of optimal utterance choices as well as the accurate inference of listeners’ choice priors.
Metalloid germanium cluster shears for lanthanide diiodides
2023-01-01

Three ionic compounds with metalloid germanium cluster [Ge 9 (Hyp) 3 ] − as free anions and polynuclear divalent lanthanide-iodide cations have been obtained and structurally characterized. A blue luminescence of Eu 2+ was found.
Fostering Effective Teaching at Schools Through Measurements of Student Perceptions: Processes, Risks and Chances
2023-01-01

Abstract Student perceptions of teaching quality have become increasingly important for measuring teaching effectiveness and can be used for the subsequent improvement of teachers’ teaching. However, measuring teaching quality through student perceptions reliably and validly and the subsequent improvement is not guaranteed. On the one hand, students’ teaching quality data are influenced by many characteristics of the students, classes and measurement instruments, and on the other hand, teachers’ use of the feedback data is influenced by factors such as personality, context and data characteristics. This chapter, therefore, provides important insights into measuring teacher effectiveness through student perceptions, risks and opportunities of using these teaching quality perceptions and the effective use of student feedback data for the development of teaching and teachers.
Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect
2023-01-01

Multilingual models such as mBERT have been demonstrated to exhibit impressive crosslingual transfer for a number of languages. Despite this, the performance drops for lowerresourced languages, especially when they are not part of the pre-training setup and when there are script differences. In this work we consider Maltese, a low-resource language of Arabic and Romance origins written in Latin script. Specifically, we investigate the impact of transliterating Maltese into Arabic scipt on a number of downstream tasks: Part-of-Speech Tagging, Dependency Parsing, and Sentiment Analysis. We compare multiple transliteration pipelines ranging from deterministic character maps to more sophisticated alternatives, including manually annotated word mappings and non-deterministic character mappings. For the latter, we show that selection techniques using n-gram language models of Tunisian Arabic, the dialect with the highest degree of mutual intelligibility to Maltese, yield better results on downstream tasks. Moreover, our experiments highlight that the use of an Arabic pre-trained model paired with transliteration outperforms mBERT. Overall, our results show that transliterating Maltese can be considered an option to improve the cross-lingual transfer capabilities.
Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis
2023-01-01

Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics.
Highly efficient β-lactamase assay applying poly-dimethylacrylamide-based surface functionalization with β-lactam antibiotics and β-lactamase inhibitors
2023-01-01

Immobilization of antibiotics to surfaces via PDMA by UV-crosslinking providing a highly efficient diagnostic β-lactamase assay. The method is transferable to different substrate materials and devices as well as antibiotics and chemical substances.
Enhancing the connectivity in a 2-node motor network using reinforcement learning-based closed-loop rTMS
2023-01-01

Abstract TMS has been extensively used to investigate the physiology of the central nervous system in health and disease. However, there is a strong need for a personalized, multi-locus, real-time stimulation procedure, which can be adaptively fine-tuned based on case-specific feedback. Here, we modulate the effective connectivity of the 2-node brain network from supplementary motor area (SMA) to primary motor cortex (M1) by closed-loop stimulation, optimized by application of an online reinforcement learning algorithm. This algorithm learns to identify the individually optimal phase of the ongoing μ-rhythm to be targeted by paired SMA-M1 TMS for maximized long-term enhancement of facilitatory effective connectivity between SMA and M1. This is one of the first demonstrations of true closed-loop stimulation, a crucially important step towards individualized highly-effective brain stimulation for therapeutic modulation of dysfunctional brain networks, e.g., the deficient SMA-M1 connection in motor stroke. Research Category and Technology and Methods Translational Research: 7. Responsive (Closed-Loop) Stimulation Keywords: EEG-TMS, Machine Learning, Closed-loop stimulation, Motor cortex
Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention
2023-01-01

This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.
From theory to reality: Evaluating smallholder farmers’ attitudes toward nature in Tanzania
2023-01-01

This paper contributes to the understanding of the value base of smallholder farmers' relationships with their environment. It aims to identify and analyze Tanzanian smallholder farmers’ attitudes toward nature.The field was approached with a combined method, a content analysis of a body of literature and a qualitative study consisting of questionnaires and interviews with smallholder farmers as well as their representatives. The literature analysis revealed three different types of evaluative attitudes toward nature in texts representing smallholder farmers’ opinions and values, and the issues affecting them. These include the Human benefit type, the Sustainable society type, and the Harmony type.The results show that there is a discrepancy among the literature analysis, the answers of smallholder farmers, and those of their representatives. The Harmony type is not mentioned in the answers of the smallholder farmers themselves and is only marginally in those of their representatives but is present in the literature.This finding is discussed from the perspective of the rights of nature as an entry point. Additionally, possible reasons for Tanzanian NGOs to include ‘foreign’ ideas, such as the Harmony type, in their agendas are discussed. Since Tanzanian smallholder farmers' evaluative attitudes toward nature are best represented by the Sustainable society type, policy strategies that support agroecology could be used to give them the mental and physical space of opportunity they need.
Long range plans to study the nuclear equation-of-state from sub- to supra-saturation densities with heavy-ion collisions
2023-01-01

We cover here the present state-of-the-art in constraining the nuclear equation-of-state (EoS) and the symmetry energy using heavy-ion collisions (HIC), from sub- to supra-saturation densities, from Fermi to (ultra-) relativistic beam energies. We also discuss how HIC constraints on the EoS contribute to the knowledge of thermodynamical properties of neutron star matter. Necessary improvements and challenges are outlined, in particular in the perspective, for HICs, of staying competitive with future astrophysical multimessenger observations.
GHisBERT – Training BERT from scratch for lexical semantic investigations across historical German language stages
2023-01-01

While static embeddings have dominated computational approaches to lexical semantic change for quite some time, recent approaches try to leverage the contextualized embeddings generated by the language model BERT for identifying semantic shifts in historical texts. However, despite their usability for detecting changes in the more recent past, it remains unclear how well language models scale to investigations going back further in time, where the language differs substantially from the training data underlying the models. In this paper, we present GHisBERT, a BERT-based language model trained from scratch on historical data covering all attested stages of German (going back to Old High German, c. 750 CE). Given a lack of ground truth data for investigating lexical semantic change across historical German language stages, we evaluate our model via a lexical similarity analysis of ten stable concepts. We show that, in comparison with an unmodified and a fine-tuned German BERT-base model, our model performs best in terms of assessing inter-concept similarity as well as intra-concept similarity over time. This in turn argues for the necessity of pre-training historical language models from scratch when working with historical linguistic data.
Lessons learned from the 1st Ariel Machine Learning Challenge: Correcting transiting exoplanet light curves for stellar spots
2023-01-01

Abstract The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterization. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is identifying the effects of spots visually and correcting them manually or discarding the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The primary focus of the paper is to present in detail a diverse arsenal of methods for doing so. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency’s upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top five winning teams, provide their code, and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal pre-processing – deep neural networks and ensemble methods – or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance.
Wasserstein t-SNE
2023-01-01

Abstract Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather than on the sample level. Units can be compared based on the distance between their means, however this ignores the within-unit distribution of samples. Here we develop an approach for exploratory analysis of hierarchical datasets using the Wasserstein distance metric that takes into account the shapes of within-unit distributions. We use t -SNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them. The distance matrix can be efficiently computed by approximating each unit with a Gaussian distribution, but we also provide a scalable method to compute exact Wasserstein distances. We use synthetic data to demonstrate the effectiveness of our Wasserstein t -SNE , and apply it to data from the 2017 German parliamentary election, considering polling stations as samples and voting districts as units. The resulting embedding uncovers meaningful structure in the data.
Clinical Course and Short-Term Follow-up of SARS-CoV-2 Vaccine–Related Myocarditis in Children and Adolescents within the Prospective German Registry for Suspected Myocarditis “MYKKE”
2023-01-01

Background: Several studies described occurrence of myocarditis after SARS-CoV-2 vaccination in pediatric patients. We aimed to characterize the clinical course of myocarditis following SARS-CoV2 vaccination including follow-up data within the prospective German registry for suspected myocarditis in children and adolescents “MYKKE.”
Different size of tympanic membranes and its functional and ecomorphological meaning in Crocidurinae and Soricinae (Lipotyphla: Soricidae)
2023-01-01

Adult skulls of Crocidura russula and Sorex araneus have been studied by μCT and by histology. The virtual representations of these skulls in ventral view display the middle ear structures very clearly. It is evident that the ectotympanic rings, which frame the tympanic membrane, are significantly larger in Crocidura than in Sorex; simple measurements and calculations reveal that the tympanic area in the two chosen skulls is significantly larger in the former taxon than in the latter (4.3 mm2 vs. 2.4 mm2). When a few more specimens from these two species as well as further taxa are added to the data set, a regression analysis shows that crocidurines have distinctly larger tympanic membranes than soricines – but there are a few exceptional species (Suncus murinus and Anourosorex planipes). Preliminary hypotheses concerning the meaning of these differences with regard to hearing performance (optimal range of frequencies) and ecomorphological adaptation (open or dense micro-habitat) are put forward. A histological cross section shows the anatomy of the ectotympanic and the tympanic membrane in Sorex araneus. It is hypothesized that the ectotympanic ring with the eardrum, which are loosely attached to the basicranium by a fibrous membrane, is not a primitive, but a derived character of soricids. The loss of a processus tympanicus could have facilitated the easy evolutionary change between ‘auditory adaptation zones’ – and perhaps explains the enormous number of soricid species.
Prospective Analysis of Versatility and User Satisfaction with a Novel Single-Use Cystoscope with Working Channel
2023-01-01

Introduction: In the present study, a prospective systematic assessment of the clinical utility of the Ambu® aScopeTM 4 Cysto Reverse Deflection with regard to image quality, maneuverability, and navigation in an outpatient and inpatient setting was performed. Materials and Methods: A prospective multicenter study was performed for evaluation of the instrument during routine cystoscopy. We evaluated the clinical performance of the instruments using a standardized user questionnaire in different categories including image quality, treatment success, imaging of all areas of the urinary bladder, quality of navigation, flexibility of the endoscope, and satisfaction with the device. Statistical analyses were performed by SPSS using the Kruskal-Wallis and Wilcoxon-Mann-Whitney tests. A p value of p ≤ 0.05 was defined as statistically significant. Results: A total of 200 cystoscopies were performed, and the questionnaire response rate was 100%. The image quality was rated as very good in 65.5% (n = 131), good in 30.5% (n = 61), and neutral in 4% (n = 8) of cases. The criteria for poor or very poor were not mentioned. The characteristic “treatment success based on image quality” was also evaluated as very good in 49% (n = 98) and good in 50.5% (n = 101). The analysis revealed a very good or good overall impression of the examiners in all cases. Replacement of the cystoscope was not necessary during any of the examinations. However, in 3 cases, technical difficulties were documented. Further analysis of the data showed that physicians with less professional experience rated the visualization of the urinary bladder (p = 0.007) and the treatment success with regard to image quality significantly worse (p = 0.007). Conclusion: The Ambu® aScopeTM 4 Cysto Reverse Deflection shows high satisfaction values among users in clinical routine. In analogy to other studies with flexible endoscopes, urologists with more professional experience show higher satisfaction values than examiners with less training in flexible endoscopy.
The Mummy Explorer—a self-regulated open-access online teaching tool
2023-01-01

Abstract Background and objectives Virtual teaching tools have gained increasing importance in recent years. In particular, the COVID-19 pandemic has reinforced the need for media-based and self-regulated tools. What is missing are tools that allow us to interlink highly interdisciplinary fields such as evolutionary medicine and, at the same time, allow us to adapt content to different lectures. Methodology We designed an interactive online teaching tool, namely, the Mummy Explorer, using open-access software (Google Web Designer), and we provided a freely downloadable template. We tested the tool on students and lecturers of evolutionary medicine using questionnaires and improved the tool according to their feedback. Results The tool has a modular design and provides an overview of a virtual mummy excavation, including the subfields of palaeopathology, paleoradiology, cultural and ethnographic context, provenance studies, paleogenetics, and physiological analyses. The template allows lecturers to generate their own versions of the tool for any topic of interest by simply changing the text and pictures. Tests undertaken with students of evolutionary medicine showed that the tool was helpful during their studies. Lecturers commented that they appreciated having a similar tool in other fields. Conclusions and implications Mummy Explorer fills a gap in the virtual teaching landscape of highly interdisciplinary fields such as evolutionary medicine. It will be offered for free download and can be adapted to any educational topic. Translations into German and possibly other languages are in progress.
Entrepreneurial Competencies in Student Companies at School: Development of a Research Instrument
2023-01-01

Abstract Entrepreneurial competencies are increasingly relevant and thus fostered in schools and universities, for instance, in the form of student companies. However, there are hardly any theoretically and empirically founded research instruments to assess students’ competence development. Accordingly, this paper aims to develop and validate a newly designed questionnaire that captures entrepreneurial competencies in three different domains: on the economic, team, and individual levels. The instrument was tested in a pilot test with 163 students and in a main study with 226 students in secondary education. Overall, reliability and the assumed factor structure could be confirmed. The questionnaire can be used in schools and universities for the purpose of quality development and competence assessment in entrepreneurship education.
SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?
2023-01-01

This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results.
CICL_DMS at SemEval-2023 Task 11: Learning With Disagreements (Le-Wi-Di)
2023-01-01

In this system paper, we describe our submission for the 11th task of SemEval2023: Learning with Disagreements, or Le-Wi-Di for short. In the task, the assumption that there is a single gold label in NLP tasks such as hate speech or misogyny detection is challenged, and instead the opinions of multiple annotators are considered. The goal is instead to capture the agreements/disagreements of the annotators. For our system, we utilize the capabilities of modern large-language models as our backbone and investigate various techniques built on top, such as ensemble learning, multi-task learning, or Gaussian processes. Our final submission shows promising results and we achieve an upper-half finish.